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View project at: https://wandb.ai/sizzing-sizzing/AWS-RL-SFT
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View project at: https://wandb.ai/sizzing-sizzing/AWS-RL-SFT
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+ "output_type": "stream",
+ "text": [
+ "Unsloth: Dropout = 0 is supported for fast patching. You are using dropout = 0.031171107608941095.\n",
+ "Unsloth will patch all other layers, except LoRA matrices, causing a performance hit.\n",
+ "/usr/local/lib/python3.12/dist-packages/unsloth/models/_utils.py:2331: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `UnslothSFTTrainer.__init__`. Use `processing_class` instead.\n",
+ " _original_trainer_init(self, *args, **kwargs)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "🦥 Unsloth: Padding-free auto-enabled, enabling faster training.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
+ " \\\\ /| Num examples = 500 | Num Epochs = 1 | Total steps = 32\n",
+ "O^O/ \\_/ \\ Batch size per device = 2 | Gradient accumulation steps = 8\n",
+ "\\ / Data Parallel GPUs = 1 | Total batch size (2 x 8 x 1) = 16\n",
+ " \"-____-\" Trainable parameters = 7,372,800 of 3,093,311,488 (0.24% trained)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "Tracking run with wandb version 0.26.0"
+ ],
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+ "text/html": [
+ "Syncing run optuna-trial-4 to Weights & Biases (docs)
"
+ ],
+ "text/plain": [
+ ""
+ ]
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+ "output_type": "display_data"
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+ "data": {
+ "text/html": [
+ " View project at https://wandb.ai/sizzing-sizzing/AWS-RL-SFT"
+ ],
+ "text/plain": [
+ ""
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+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [32/32 03:03, Epoch 1/1]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | Epoch | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1 | \n",
+ " 0.114800 | \n",
+ " 0.079328 | \n",
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\n",
+ " \n",
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"
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+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [20/20 00:12]\n",
+ "
Run history:
| eval/loss | ▁█ |
| eval/runtime | █▁ |
| eval/samples_per_second | ▁█ |
| eval/steps_per_second | ▁█ |
| train/epoch | ▁███ |
| train/global_step | ▁███ |
| train/grad_norm | ▁ |
| train/learning_rate | ▁ |
| train/loss | ▁ |
Run summary:
| eval/loss | 0.07934 |
| eval/runtime | 13.4903 |
| eval/samples_per_second | 5.93 |
| eval/steps_per_second | 1.483 |
| total_flos | 2946293207040000.0 |
| train/epoch | 1 |
| train/global_step | 32 |
| train/grad_norm | 0.14956 |
| train/learning_rate | 4e-05 |
| train/loss | 0.1148 |
| +4 | ... |
View project at: https://wandb.ai/sizzing-sizzing/AWS-RL-SFT
Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+ ],
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+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Find logs at: ./wandb/run-20260423_001430-wvylgkh8/logs"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "output_type": "stream",
+ "text": [
+ "[I 2026-04-23 00:17:57,673] Trial 4 finished with value: 0.0793430432677269 and parameters: {'lora_r': 16, 'lora_alpha_mul': 1, 'lora_dropout': 0.031171107608941095, 'learning_rate': 0.00023094679892576625, 'warmup_ratio': 0.03}. Best is trial 0 with value: 0.05230738967657089.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Trial 4: r=16 alpha=16 dropout=0.031 lr=2.31e-04 warmup=0.03 -> eval_loss=0.0793\n",
+ "==((====))== Unsloth 2026.4.7: Fast Qwen2 patching. Transformers: 4.57.6.\n",
+ " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n",
+ "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.35. FA2 = False]\n",
+ " \"-____-\" Free license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Unsloth: Dropout = 0 is supported for fast patching. You are using dropout = 0.00884925020519195.\n",
+ "Unsloth will patch all other layers, except LoRA matrices, causing a performance hit.\n",
+ "/usr/local/lib/python3.12/dist-packages/unsloth/models/_utils.py:2331: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `UnslothSFTTrainer.__init__`. Use `processing_class` instead.\n",
+ " _original_trainer_init(self, *args, **kwargs)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "🦥 Unsloth: Padding-free auto-enabled, enabling faster training.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
+ " \\\\ /| Num examples = 500 | Num Epochs = 1 | Total steps = 32\n",
+ "O^O/ \\_/ \\ Batch size per device = 2 | Gradient accumulation steps = 8\n",
+ "\\ / Data Parallel GPUs = 1 | Total batch size (2 x 8 x 1) = 16\n",
+ " \"-____-\" Trainable parameters = 3,686,400 of 3,089,625,088 (0.12% trained)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "data": {
+ "text/html": [
+ "Tracking run with wandb version 0.26.0"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Run data is saved locally in /content/wandb/run-20260423_001819-n06qegrd"
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+ "data": {
+ "text/html": [
+ "Syncing run optuna-trial-5 to Weights & Biases (docs)
"
+ ],
+ "text/plain": [
+ ""
+ ]
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+ "metadata": {},
+ "output_type": "display_data"
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+ {
+ "data": {
+ "text/html": [
+ " View project at https://wandb.ai/sizzing-sizzing/AWS-RL-SFT"
+ ],
+ "text/plain": [
+ ""
+ ]
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+ "output_type": "display_data"
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+ {
+ "data": {
+ "text/html": [
+ " View run at https://wandb.ai/sizzing-sizzing/AWS-RL-SFT/runs/n06qegrd"
+ ],
+ "text/plain": [
+ ""
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+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [32/32 03:15, Epoch 1/1]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | Epoch | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 1 | \n",
+ " 0.122500 | \n",
+ " 0.082837 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ],
+ "text/plain": [
+ ""
+ ]
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+ "metadata": {},
+ "output_type": "display_data"
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+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [20/20 00:12]\n",
+ "
Run history:
| eval/loss | █▁ |
| eval/runtime | █▁ |
| eval/samples_per_second | ▁█ |
| eval/steps_per_second | ▁█ |
| train/epoch | ▁███ |
| train/global_step | ▁███ |
| train/grad_norm | ▁ |
| train/learning_rate | ▁ |
| train/loss | ▁ |
Run summary:
| eval/loss | 0.08283 |
| eval/runtime | 13.5748 |
| eval/samples_per_second | 5.893 |
| eval/steps_per_second | 1.473 |
| total_flos | 2942389309440000.0 |
| train/epoch | 1 |
| train/global_step | 32 |
| train/grad_norm | 0.32769 |
| train/learning_rate | 3e-05 |
| train/loss | 0.1225 |
| +4 | ... |
View project at: https://wandb.ai/sizzing-sizzing/AWS-RL-SFT
Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
+ ],
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+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "Find logs at: ./wandb/run-20260423_001819-n06qegrd/logs"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "output_type": "stream",
+ "text": [
+ "[I 2026-04-23 00:21:57,943] Trial 5 finished with value: 0.08283159881830215 and parameters: {'lora_r': 8, 'lora_alpha_mul': 4, 'lora_dropout': 0.00884925020519195, 'learning_rate': 0.0001370838023704289, 'warmup_ratio': 0.1}. Best is trial 0 with value: 0.05230738967657089.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " Trial 5: r=8 alpha=32 dropout=0.009 lr=1.37e-04 warmup=0.1 -> eval_loss=0.0828\n",
+ "\n",
+ "=== OPTUNA RESULTS ===\n",
+ "Best eval_loss : 0.0523\n",
+ "Best trial : #0\n",
+ "Best params :\n",
+ " lora_r 16\n",
+ " lora_alpha_mul 1\n",
+ " lora_dropout 0.005808361216819946\n",
+ " learning_rate 0.00040311702880369243\n",
+ " warmup_ratio 0.1\n"
+ ]
+ }
+ ],
"source": [
"study = optuna.create_study(\n",
" direction='minimize',\n",
@@ -627,44 +3101,57 @@
{
"cell_type": "markdown",
"id": "21d9c33e",
- "metadata": {},
+ "metadata": {
+ "id": "21d9c33e"
+ },
"source": [
"## 10. Optuna plots\n",
"\n",
- "Three views for the judges:\n",
+ "Five views, all rendered with matplotlib and saved as PNGs to `OUT_DIR/plots/`:\n",
"\n",
- "1. **Optimization history** \u2014 did later trials converge on lower loss?\n",
- "2. **Parallel coordinates** \u2014 which hparam combinations trend toward low loss?\n",
- "3. **Param importances** \u2014 which knob mattered most?"
+ "1. **Optimization history** — did later trials converge on lower loss?\n",
+ "2. **Param importances** — which knob mattered most (fANOVA-style)?\n",
+ "3. **Parallel coordinates** — which hparam combinations trend toward low loss?\n",
+ "4. **Slice plot** — per-parameter scatter against the objective.\n",
+ "5. **Per-trial loss curves** — overlay every trial's training/eval loss to see how the search space behaved during fitting."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "90d27d5b",
- "metadata": {},
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "90d27d5b",
+ "outputId": "96cdda6f-79f0-44fc-dd02-70368ed5bff9"
+ },
"outputs": [],
"source": [
- "from optuna.visualization import (\n",
- " plot_optimization_history, plot_parallel_coordinate, plot_param_importances,\n",
- ")\n",
- "\n",
- "plot_optimization_history(study).show()\n",
- "plot_parallel_coordinate(study).show()\n",
- "plot_param_importances(study).show()"
+ "# All Optuna views as matplotlib PNGs in OUT_DIR/plots/.\n",
+ "# Each helper renders inline AND saves to disk; see PLOTS_DIR for the files.\n",
+ "plot_optuna_history(study)\n",
+ "plot_optuna_param_importances(study)\n",
+ "plot_optuna_parallel_coordinates(study)\n",
+ "plot_optuna_slice(study)\n",
+ "plot_optuna_trial_loss_curves(study)"
]
},
{
"cell_type": "markdown",
"id": "7323c278",
- "metadata": {},
+ "metadata": {
+ "id": "7323c278"
+ },
"source": [
- "## 11. Final SFT run \u2014 best hparams, full data, checkpointed\n",
+ "## 11. Final SFT run — best hparams, full data, checkpointed\n",
"\n",
"- Full dataset (1500 train / 150 val)\n",
"- 2 epochs (vs. 1 during search)\n",
"- Checkpoints saved every 50 steps; last 3 kept (`save_total_limit=3`)\n",
- "- `load_best_model_at_end=True` \u2014 final model = lowest eval_loss checkpoint, not last\n",
+ "- `load_best_model_at_end=True` — final model = lowest eval_loss checkpoint, not last\n",
"- **Resume-safe**: if the session dies, rerunning this cell picks up from the latest `checkpoint-*/`"
]
},
@@ -672,7 +3159,104 @@
"cell_type": "code",
"execution_count": null,
"id": "cbeb9145",
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+ "metadata": {
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+ ]
+ },
+ "id": "cbeb9145",
+ "outputId": "0f4b26e4-8a29-4de8-a86e-44de8c7831f4"
+ },
"outputs": [],
"source": [
"# Ensure the Transformers 5.x shim is in place (idempotent).\n",
@@ -716,7 +3300,7 @@
"# --- Render the full dataset with the chat template ---\n",
"full_train = ds['train'].map(lambda ex: _render_messages(ex, tokenizer))\n",
"full_val = ds['validation'].map(lambda ex: _render_messages(ex, tokenizer))\n",
- "# Drop metadata then pre-tokenize \u2014 leaves only numeric columns for the collator.\n",
+ "# Drop metadata then pre-tokenize — leaves only numeric columns for the collator.\n",
"full_train = full_train.remove_columns([c for c in full_train.column_names if c != 'text'])\n",
"full_val = full_val.remove_columns([c for c in full_val.column_names if c != 'text'])\n",
"def _tokenize_batch(batch):\n",
@@ -739,9 +3323,9 @@
" resume_ckpt = str(existing[-1])\n",
" print(f'Resuming from checkpoint: {resume_ckpt}')\n",
" else:\n",
- " print('No checkpoint found \u2014 starting fresh')\n",
+ " print('No checkpoint found — starting fresh')\n",
"else:\n",
- " print('No prior training dir \u2014 starting fresh')\n",
+ " print('No prior training dir — starting fresh')\n",
"\n",
"final_trainer = SFTTrainer(\n",
" model=model,\n",
@@ -772,13 +3356,13 @@
" load_best_model_at_end=True,\n",
" metric_for_best_model='eval_loss',\n",
" greater_is_better=False,\n",
- " report_to='wandb',\n",
+ " report_to='none',\n",
" run_name='final-sft',\n",
" seed=CONFIG['seed'],\n",
" dataset_num_proc=1,\n",
" ),\n",
")\n",
- "# Completion-only loss \u2014 same masking as Optuna trials\n",
+ "# Completion-only loss — same masking as Optuna trials\n",
"final_trainer = train_on_responses_only(\n",
" final_trainer,\n",
" instruction_part='<|im_start|>user\\n',\n",
@@ -793,9 +3377,39 @@
},
{
"cell_type": "markdown",
+ "id": "707ab49e",
+ "source": [
+ "## 11a. Final SFT — training curves\n",
+ "\n",
+ "The trainer's `log_history` carries every logged step (loss, eval_loss, learning_rate, grad_norm). Render it as a 2×2 grid and save to `OUT_DIR/plots/final_training_curves.png`."
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "id": "fa46b4b1",
+ "source": [
+ "# Final SFT — training curves\n",
+ "# (train + eval loss, learning-rate schedule, gradient-norm) extracted from\n",
+ "# trainer.state.log_history. Saved to OUT_DIR/plots/final_training_curves.png.\n",
+ "plot_training_curves(\n",
+ " list(final_trainer.state.log_history),\n",
+ " title='Final SFT — training curves (full dataset, best Optuna params)',\n",
+ " save_name='final_training_curves',\n",
+ ")"
+ ],
"metadata": {},
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "kRib4wOVkQdh",
+ "metadata": {
+ "id": "kRib4wOVkQdh"
+ },
"source": [
- "## 12. Post-SFT eval \u2014 the delta that matters\n",
+ "## 12. Post-SFT eval — the delta that matters\n",
"\n",
"Same prompts as the baseline. This is the headline table for judges."
]
@@ -803,7 +3417,14 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "id": "uOmvY2y_kQdh",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "uOmvY2y_kQdh",
+ "outputId": "e33f9f51-2439-43c9-b9a9-708abb482bfa"
+ },
"outputs": [],
"source": [
"FastLanguageModel.for_inference(model)\n",
@@ -832,18 +3453,18 @@
" 'delta': {k: posttrain_metrics[k] - baseline_metrics[k] for k in metric_keys},\n",
" }, f, indent=2)\n",
"\n",
- "# Log the headline deltas to wandb so they show up in the final run's summary panel\n",
- "wandb.log({\n",
- " 'delta/format_pct': posttrain_metrics['format_pct'] - baseline_metrics['format_pct'],\n",
- " 'delta/exact_pct': posttrain_metrics['exact_pct'] - baseline_metrics['exact_pct'],\n",
- " 'delta/operation_pct': posttrain_metrics['operation_pct'] - baseline_metrics['operation_pct'],\n",
- " 'delta/service_pct': posttrain_metrics['service_pct'] - baseline_metrics['service_pct'],\n",
- "})"
+ "# Render baseline-vs-SFT grouped bars + Δpp delta bars; saved to\n",
+ "# OUT_DIR/plots/eval_metrics_comparison.png. The JSON dump above keeps the\n",
+ "# raw numbers for any downstream comparison.\n",
+ "plot_eval_comparison(baseline_metrics, posttrain_metrics)"
]
},
{
"cell_type": "markdown",
- "metadata": {},
+ "id": "Wp7ycEItkQdh",
+ "metadata": {
+ "id": "Wp7ycEItkQdh"
+ },
"source": [
"## 13. Inspect a few post-SFT generations\n",
"\n",
@@ -853,8 +3474,41 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
- "outputs": [],
+ "id": "OovPADlKkQdh",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "OovPADlKkQdh",
+ "outputId": "d50dd10c-4135-49f6-fe79-a8a7ef7bb807"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Post-SFT generations vs canonical:\n",
+ "\n",
+ " [OK] expected : 'aws route53 list-hosted-zones'\n",
+ " generated: 'aws route53 list-hosted-zones'\n",
+ "\n",
+ " [OK] expected : 'aws dynamodb put-item --table-name orders --item \\'{\"order_id\":{\"S\":\"001\"},\"status\":{\"S\":\"pending\"}}\\''\n",
+ " generated: 'aws dynamodb put-item --table-name orders --item \\'{\"order_id\":{\"S\":\"001\"},\"status\":{\"S\":\"pending\"}}\\''\n",
+ "\n",
+ " [~ ] expected : 'aws help --task-hint'\n",
+ " generated: 'aws lambda create-function --function-name scheduled-task --runtime python3.12 --handler index.handl'\n",
+ "\n",
+ " [OK] expected : 'aws sns create-topic --name notifications'\n",
+ " generated: 'aws sns create-topic --name notifications'\n",
+ "\n",
+ " [OK] expected : 'aws apigatewayv2 create-api --name payments-api --protocol-type HTTP'\n",
+ " generated: 'aws apigatewayv2 create-api --name payments-api --protocol-type HTTP'\n",
+ "\n",
+ " [OK] expected : 'aws s3api create-bucket --bucket firehose-delivery'\n",
+ " generated: 'aws s3api create-bucket --bucket firehose-delivery'\n"
+ ]
+ }
+ ],
"source": [
"print('Post-SFT generations vs canonical:')\n",
"for r in posttrain_metrics['_per_row'][:6]:\n",
@@ -865,18 +3519,228 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "id": "7vLaN97EkQdh",
+ "metadata": {
+ "id": "7vLaN97EkQdh"
+ },
"source": [
"## 14. Push LoRA adapter to Hugging Face Hub\n",
"\n",
- "Just the adapter \u2014 ~60MB instead of the full 3B (~6GB). Consumers will apply it on top of `Qwen/Qwen2.5-Coder-3B-Instruct` at inference time."
+ "Just the adapter — ~60MB instead of the full 3B (~6GB). Consumers will apply it on top of `Qwen/Qwen2.5-Coder-3B-Instruct` at inference time."
]
},
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
- "outputs": [],
+ "id": "HzjnMLBMkQdh",
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+ "height": 315,
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+ },
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+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Adapter saved locally: /content/out/adapter\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "b15fcea90a104c5a8fa21646638b0a12",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "README.md: 0%| | 0.00/573 [00:00` reasoning (R1-Zero style)\n",
"\n",
- "The adapter we just trained serves as both the starting policy and the KL anchor \u2014 SFT locks format and basic competence, GRPO refines task correctness with real reward signal."
+ "The adapter we just trained serves as both the starting policy and the KL anchor — SFT locks format and basic competence, GRPO refines task correctness with real reward signal."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "RsaNFJOpz3qj",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 106
+ },
+ "id": "RsaNFJOpz3qj",
+ "outputId": "1b6ff873-a8a0-4844-93be-4f45c7ff2984"
+ },
+ "outputs": [],
+ "source": [
+ "import shutil\n",
+ "from pathlib import Path\n",
+ "\n",
+ "STAGE = Path('/content/aws_rl_sft_export')\n",
+ "STAGE.mkdir(exist_ok=True)\n",
+ "\n",
+ "# Copy the output folder wholesale. `out/plots/` is included automatically.\n",
+ "for folder in ['out']:\n",
+ " src = Path('/content') / folder\n",
+ " if src.exists():\n",
+ " dest = STAGE / folder\n",
+ " if dest.exists():\n",
+ " shutil.rmtree(dest)\n",
+ " shutil.copytree(src, dest)\n",
+ " size_mb = sum(p.stat().st_size for p in dest.rglob('*') if p.is_file()) / 1e6\n",
+ " print(f' {folder}/ {size_mb:.1f} MB')\n",
+ " else:\n",
+ " print(f' {folder}/ MISSING')\n",
+ "\n",
+ "# Zip it\n",
+ "zip_path = shutil.make_archive('/content/aws_rl_sft_artifacts', 'zip', STAGE)\n",
+ "size_mb = Path(zip_path).stat().st_size / 1e6\n",
+ "print(f'\\nArchive: {zip_path} ({size_mb:.1f} MB)')\n",
+ "\n",
+ "# Download\n",
+ "from google.colab import files\n",
+ "files.download(zip_path)"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "Wkhwg6Wqz547",
+ "metadata": {
+ "id": "Wkhwg6Wqz547"
+ },
+ "outputs": [],
+ "source": []
}
],
"metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -935,9 +3860,13013 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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+++ b/train_grpo.py
@@ -0,0 +1,1283 @@
+"""GRPO training for the AWS RL environment — multi-turn rollouts + parallel envs.
+
+Mirrors the kube-sre-gym training pattern (heavy logic in this module, thin
+notebook on top):
+ - Each "episode" runs up to MAX_TURNS steps.
+ - Each step = one ``aws ...`` command; the command's stdout/stderr is fed
+ back into the next turn's prompt as the user message.
+ - Each GRPO step picks ONE curriculum task and runs G concurrent rollouts
+ (one per env in MultiTurnEnvPool) sharing that task.
+ - prompt_ids / completion_ids / logprobs are accumulated across turns so
+ GRPO assigns episode-level reward to the full token sequence.
+
+Usage (CLI)::
+
+ # Single training pass with explicit hyperparams
+ python train_grpo.py --mode train \\
+ --env-url http://localhost:8000 \\
+ --num-generations 8 --max-turns 6 --max-steps 200
+
+ # Optuna search over hyperparams, then dump best_cfg.json
+ python train_grpo.py --mode optuna --n-trials 6
+
+ # Optuna search, then full-length retrain using the best config
+ python train_grpo.py --mode full --n-trials 6 --max-steps 200
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import csv
+import gc
+import json
+import logging
+import re
+import threading
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Callable, Optional
+
+import torch
+from datasets import Dataset
+from peft import LoraConfig, PeftModel
+from transformers import AutoTokenizer
+from trl import GRPOConfig, GRPOTrainer
+
+from client import AwsRlEnv
+from models import AwsRlAction, AwsRlObservation, Task, TaskDifficulty, TaskID
+from server.services.curriculum import Curriculum
+
+logger = logging.getLogger(__name__)
+
+
+# ============================================================
+# System prompt — multi-turn AWS CLI agent
+# ============================================================
+
+SYSTEM_PROMPT = """You are an expert AWS Operations agent. You operate a simulated AWS cloud by emitting ONE AWS CLI command per turn.
+
+The user message contains:
+ - The task description.
+ - (Optional) A history of your previous commands and their outputs from earlier in this episode — use them to decide your next move.
+ - The most recent observation (last command's stdout / stderr / progress).
+
+Each turn:
+ 1. Optionally reason inside a single ... block. Keep it concise.
+ 2. After , on a NEW LINE, output EXACTLY ONE AWS CLI command starting with "aws ".
+
+Hard rules:
+ - The command line must contain ONLY the command — no markdown, no backticks, no quotes around it, no trailing commentary.
+ - If a command failed last turn, try a DIFFERENT approach. Do not repeat the exact same command twice in a row.
+ - When the task description names a specific resource (a bucket, table, queue, etc.), use that exact name.
+"""
+
+
+DEFAULT_CFG: dict[str, Any] = {
+ "learning_rate": 5e-6,
+ "beta": 0.04,
+ "num_generations": 8,
+ "temperature": 0.9,
+ "top_p": 0.95,
+ "lora_r": 16,
+ "lora_alpha_mul": 2,
+ "max_turns": 6,
+}
+
+
+# ============================================================
+# Helpers — prompt formatting + command parsing
+# ============================================================
+
+_THINK_BLOCK = re.compile(r"]*>.*?", re.DOTALL | re.IGNORECASE)
+_OPEN_THINK = re.compile(r"]*>.*", re.DOTALL | re.IGNORECASE)
+
+
+def extract_aws_command(raw: str) -> str:
+ """Strip blocks + markdown fences, return the first ``aws ...`` line.
+
+ Falls back to ``aws help`` so the env always gets a syntactically valid
+ command (the env will just produce a help-text observation, which is a
+ better RL signal than a parse error).
+ """
+ cleaned = _THINK_BLOCK.sub("", raw)
+ cleaned = _OPEN_THINK.sub("", cleaned)
+ for line in cleaned.splitlines():
+ line = line.strip().strip("`").strip()
+ if line.startswith("aws "):
+ return line
+ return "aws help"
+
+
+def _truncate(text: str, n: int) -> str:
+ if not text:
+ return ""
+ if len(text) <= n:
+ return text
+ return text[: n - 3] + "..."
+
+
+def format_observation(obs: AwsRlObservation) -> str:
+ """Render the latest env observation as a compact text block."""
+ parts: list[str] = []
+ if obs.command_output:
+ parts.append(f"Output:\n{_truncate(obs.command_output, 800)}")
+ if obs.error:
+ parts.append(f"Error:\n{_truncate(obs.error, 400)}")
+ parts.append(
+ f"Progress: {obs.partial_progress:.2f} "
+ f"Achieved: {obs.task_achieved} Step: {obs.step_count}"
+ )
+ if obs.hint_text:
+ parts.append(f"Hint: {_truncate(obs.hint_text, 200)}")
+ return "\n".join(parts)
+
+
+def format_history(history: list[dict], keep_last: int = 6) -> str:
+ """Render the last ``keep_last`` (cmd, output, reward) tuples for context."""
+ if not history:
+ return ""
+ recent = history[-keep_last:]
+ rendered: list[str] = ["PREVIOUS COMMANDS:"]
+ for i, h in enumerate(recent, start=max(1, len(history) - keep_last + 1)):
+ rendered.append(
+ f"[{i}] $ {h['command']}\n"
+ f" output: {_truncate(h['output'], 300)}\n"
+ f" reward: {h['reward']:.2f}"
+ )
+ return "\n".join(rendered)
+
+
+def apply_chat_template(tokenizer: AutoTokenizer, messages: list[dict]) -> str:
+ """Apply a chat template; fall back to a plain rendering if none is set."""
+ if getattr(tokenizer, "chat_template", None):
+ try:
+ return tokenizer.apply_chat_template(
+ messages, add_generation_prompt=True, tokenize=False
+ )
+ except TypeError:
+ return tokenizer.apply_chat_template(messages, tokenize=False)
+ parts: list[str] = []
+ for m in messages:
+ parts.append(f"<|{m['role']}|>\n{m['content']}\n")
+ parts.append("<|assistant|>\n")
+ return "".join(parts)
+
+
+def build_user_prompt(task: Task, obs: AwsRlObservation, history: list[dict]) -> str:
+ desc = task.description
+ if task.desired_state_spec:
+ desc = f"{desc}\n\nDesired end state:\n{task.desired_state_spec}"
+ history_text = format_history(history)
+ obs_text = format_observation(obs)
+ if history_text:
+ return f"TASK: {desc}\n\n{history_text}\n\n---\n\nCURRENT OBSERVATION:\n{obs_text}"
+ return f"TASK: {desc}\n\nCURRENT OBSERVATION:\n{obs_text}"
+
+
+# ============================================================
+# Policy loading — Unsloth 4-bit base + LoRA-from-SFT-adapter
+# ============================================================
+
+
+@dataclass
+class PolicySpec:
+ base_model: str = "unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit"
+ sft_adapter: str = "Sizzing/aws-rl-sft-qwen25coder3b-adapter"
+ max_seq_length: int = 3072
+
+
+def load_policy(
+ base_model: str,
+ sft_adapter: Optional[str] = None,
+ max_seq_length: int = 3072,
+ trainable: bool = True,
+):
+ """Load Unsloth 4-bit base + (optional) LoRA adapter from the SFT run.
+
+ ``trainable=True`` returns a PeftModel ready for GRPO training (Unsloth's
+ training kernels enabled, input require-grads hook installed).
+ ``trainable=False`` returns the same stack in inference mode for eval.
+ """
+ from unsloth import FastLanguageModel
+
+ base, tokenizer = FastLanguageModel.from_pretrained(
+ model_name=base_model,
+ max_seq_length=max_seq_length,
+ load_in_4bit=True,
+ )
+ if sft_adapter:
+ model = PeftModel.from_pretrained(base, sft_adapter, is_trainable=trainable)
+ else:
+ # No adapter: GRPOTrainer can attach a fresh LoRA via peft_config later.
+ model = base
+
+ if trainable:
+ FastLanguageModel.for_training(model)
+ if hasattr(model, "enable_input_require_grads"):
+ model.enable_input_require_grads()
+ else:
+ FastLanguageModel.for_inference(model)
+
+ if tokenizer.pad_token is None:
+ tokenizer.pad_token = tokenizer.eos_token
+
+ return model, tokenizer
+
+
+def free_model(model) -> None:
+ """Release VRAM held by ``model`` and any captured optimizer state."""
+ try:
+ del model
+ except Exception:
+ pass
+ gc.collect()
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+
+
+# ============================================================
+# Multi-turn rollout — one episode in one env
+# ============================================================
+
+
+@dataclass
+class SamplingCfg:
+ temperature: float = 0.9
+ top_p: float = 0.95
+ max_new_tokens: int = 256
+ max_prompt_length: int = 2048
+
+
+_GENERATE_LOCK = threading.Lock()
+"""Serialise model.generate() calls across the asyncio.gather rollout group.
+
+The model lives on a single GPU; concurrent generate() calls would collide.
+We let the env step run concurrently (the slow part — WebSocket round-trip +
+MiniStack execution); only the generation is serialised.
+"""
+
+
+def _generate_with_logprobs(
+ model,
+ tokenizer,
+ prompt_text: str,
+ sampling: SamplingCfg,
+) -> tuple[list[int], list[int], list[float]]:
+ """Generate one completion + return per-token logprobs.
+
+ Returns: (prompt_ids, completion_ids, completion_logprobs).
+ """
+ with _GENERATE_LOCK:
+ prompt_input = tokenizer(
+ prompt_text,
+ return_tensors="pt",
+ truncation=True,
+ max_length=sampling.max_prompt_length,
+ ).to(model.device)
+
+ was_training = model.training
+ model.eval()
+ try:
+ with torch.no_grad():
+ gen_out = model.generate(
+ **prompt_input,
+ max_new_tokens=sampling.max_new_tokens,
+ do_sample=True,
+ temperature=sampling.temperature,
+ top_p=sampling.top_p,
+ return_dict_in_generate=True,
+ output_scores=True,
+ pad_token_id=tokenizer.pad_token_id,
+ )
+ finally:
+ if was_training:
+ model.train()
+
+ prompt_ids = prompt_input.input_ids[0].tolist()
+ prompt_len = len(prompt_ids)
+ completion_seq = gen_out.sequences[0, prompt_len:].tolist()
+
+ # Per-token logprobs from raw logits.
+ logprobs: list[float] = []
+ for i, scores_t in enumerate(gen_out.scores):
+ if i >= len(completion_seq):
+ break
+ lp = torch.log_softmax(scores_t[0].float(), dim=-1)
+ logprobs.append(float(lp[completion_seq[i]].item()))
+
+ return prompt_ids, completion_seq, logprobs
+
+
+async def rollout_one_episode(
+ env: AwsRlEnv,
+ task: Task,
+ model,
+ tokenizer,
+ system_prompt: str,
+ max_turns: int,
+ max_total_tokens: int,
+ sampling: SamplingCfg,
+) -> dict:
+ """Run one multi-turn episode in one env, accumulating tokens across turns."""
+ try:
+ res = await env.reset(task=task)
+ except Exception as e:
+ logger.warning("reset() failed for task=%s: %s", task.task_id, e)
+ return {
+ "prompt_ids": [],
+ "completion_ids": [],
+ "logprobs": [],
+ "task_reward": -1.0,
+ "task_achieved": False,
+ "final_progress": 0.0,
+ "num_steps": 0,
+ "transcript": [{"error": f"reset failed: {e!r}"}],
+ }
+ obs: AwsRlObservation = res.observation
+
+ prompt_ids: list[int] = []
+ completion_ids: list[int] = []
+ logprobs: list[float] = []
+ step_rewards: list[float] = []
+ history: list[dict] = []
+ final_progress = float(getattr(obs, "partial_progress", 0.0) or 0.0)
+ final_achieved = bool(getattr(obs, "task_achieved", False))
+
+ for _turn in range(max_turns):
+ if res.done:
+ break
+ if len(completion_ids) >= max_total_tokens:
+ break
+
+ user_text = build_user_prompt(task, obs, history)
+ messages = [
+ {"role": "system", "content": system_prompt},
+ {"role": "user", "content": user_text},
+ ]
+ prompt_text = apply_chat_template(tokenizer, messages)
+
+ # Generation runs on the calling thread (blocking) but env.step calls
+ # for other rollouts in this group can overlap because they're all
+ # awaiting in the same loop.
+ loop = asyncio.get_running_loop()
+ turn_prompt_ids, turn_completion_ids, turn_logprobs = await loop.run_in_executor(
+ None, _generate_with_logprobs, model, tokenizer, prompt_text, sampling
+ )
+ completion_text = tokenizer.decode(turn_completion_ids, skip_special_tokens=True)
+ cmd = extract_aws_command(completion_text)
+
+ try:
+ res = await env.step(AwsRlAction(command=cmd))
+ step_reward = float(res.reward or 0.0)
+ except Exception as e:
+ logger.warning("step() error on cmd=%r: %s", cmd[:80], e)
+ step_reward = -0.1
+ history.append(
+ {
+ "command": cmd,
+ "output": f"ERROR: {e!r}",
+ "reward": step_reward,
+ }
+ )
+ prompt_ids.extend(turn_prompt_ids)
+ completion_ids.extend(turn_completion_ids)
+ logprobs.extend(turn_logprobs)
+ step_rewards.append(step_reward)
+ break
+
+ prompt_ids.extend(turn_prompt_ids)
+ completion_ids.extend(turn_completion_ids)
+ logprobs.extend(turn_logprobs)
+ step_rewards.append(step_reward)
+ obs = res.observation
+ final_progress = float(getattr(obs, "partial_progress", 0.0) or 0.0)
+ final_achieved = bool(getattr(obs, "task_achieved", False))
+ history.append(
+ {
+ "command": cmd,
+ "output": _truncate(getattr(obs, "command_output", "") or "", 500),
+ "reward": step_reward,
+ }
+ )
+
+ return {
+ "prompt_ids": prompt_ids,
+ "completion_ids": completion_ids,
+ "logprobs": logprobs,
+ "task_reward": float(sum(step_rewards)) if step_rewards else -1.0,
+ "task_achieved": final_achieved,
+ "final_progress": final_progress,
+ "num_steps": len(history),
+ "transcript": history,
+ "task_id": int(task.task_id),
+ "difficulty": task.difficulty.value,
+ }
+
+
+# ============================================================
+# MultiTurnEnvPool — sync wrapper around N async env sessions
+# ============================================================
+
+
+class MultiTurnEnvPool:
+ """N persistent WebSocket env sessions, exposed via a sync ``run_group`` API.
+
+ Owns a background thread running an asyncio loop. Connect / close happens
+ once for the lifetime of training. Submitted coroutines run in the
+ background loop via ``asyncio.run_coroutine_threadsafe`` and the calling
+ thread blocks on the resulting concurrent.futures.Future.
+ """
+
+ def __init__(self, base_url: str, size: int, timeout_s: float = 120.0) -> None:
+ if size < 1:
+ raise ValueError("size must be >= 1")
+ self.base_url = base_url
+ self.size = size
+ self.timeout_s = timeout_s
+ self._envs: list[AwsRlEnv] = []
+ self._loop: Optional[asyncio.AbstractEventLoop] = None
+ self._thread: Optional[threading.Thread] = None
+ self._ready = threading.Event()
+ self._setup_error: Optional[BaseException] = None
+
+ def start(self) -> None:
+ """Open N WebSocket sessions on the background loop."""
+ if self._thread is not None:
+ return
+
+ def run() -> None:
+ loop = asyncio.new_event_loop()
+ self._loop = loop
+ asyncio.set_event_loop(loop)
+ try:
+ loop.run_until_complete(self._connect_all())
+ except BaseException as e:
+ self._setup_error = e
+ self._ready.set()
+ return
+ self._ready.set()
+ loop.run_forever()
+
+ self._thread = threading.Thread(target=run, daemon=True, name="env-pool")
+ self._thread.start()
+ self._ready.wait()
+ if self._setup_error is not None:
+ raise RuntimeError(
+ f"MultiTurnEnvPool failed to connect {self.size} sessions to "
+ f"{self.base_url}: {self._setup_error!r}"
+ )
+ logger.info("MultiTurnEnvPool: %d sessions on %s", self.size, self.base_url)
+
+ async def _connect_all(self) -> None:
+ envs = [AwsRlEnv(base_url=self.base_url) for _ in range(self.size)]
+ try:
+ await asyncio.gather(*(e.connect() for e in envs))
+ except BaseException:
+ await asyncio.gather(*(e.close() for e in envs), return_exceptions=True)
+ raise
+ self._envs = envs
+
+ def close(self) -> None:
+ if self._thread is None or self._loop is None:
+ return
+ loop = self._loop
+
+ async def _shutdown() -> None:
+ await asyncio.gather(
+ *(e.close() for e in self._envs), return_exceptions=True
+ )
+
+ try:
+ fut = asyncio.run_coroutine_threadsafe(_shutdown(), loop)
+ fut.result(timeout=10.0)
+ except Exception as e:
+ logger.warning("Pool shutdown error (ignored): %s", e)
+ finally:
+ loop.call_soon_threadsafe(loop.stop)
+ self._thread.join(timeout=5.0)
+ self._thread = None
+ self._loop = None
+ self._envs = []
+
+ def run_group(
+ self,
+ task: Task,
+ model,
+ tokenizer,
+ system_prompt: str,
+ max_turns: int,
+ max_total_tokens: int,
+ sampling: SamplingCfg,
+ ) -> list[dict]:
+ """Run N concurrent multi-turn rollouts on the same task. Sync; blocks."""
+ assert self._loop is not None and self._envs, "call start() first"
+
+ async def _gather() -> list[dict]:
+ return list(
+ await asyncio.gather(
+ *(
+ rollout_one_episode(
+ env,
+ task,
+ model,
+ tokenizer,
+ system_prompt,
+ max_turns,
+ max_total_tokens,
+ sampling,
+ )
+ for env in self._envs
+ )
+ )
+ )
+
+ fut = asyncio.run_coroutine_threadsafe(_gather(), self._loop)
+ return fut.result(timeout=self.timeout_s * max(1, max_turns))
+
+ def __enter__(self) -> "MultiTurnEnvPool":
+ self.start()
+ return self
+
+ def __exit__(self, *exc) -> None:
+ self.close()
+
+
+# ============================================================
+# Reward functions (TRL convention) + rollout_func factory
+# ============================================================
+
+
+def reward_task(completions: list[str], **kwargs) -> list[float]:
+ rewards = kwargs.get("task_reward")
+ if rewards is None:
+ return [0.0 for _ in completions]
+ return [float(r) for r in rewards]
+
+
+def reward_achieved(completions: list[str], **kwargs) -> list[float]:
+ flags = kwargs.get("task_achieved")
+ if flags is None:
+ return [0.0 for _ in completions]
+ return [float(f) for f in flags]
+
+
+def reward_progress(completions: list[str], **kwargs) -> list[float]:
+ progress = kwargs.get("final_progress")
+ if progress is None:
+ return [0.0 for _ in completions]
+ return [float(p) for p in progress]
+
+
+def make_rollout_func(
+ curriculum: Curriculum,
+ pool: MultiTurnEnvPool,
+ model,
+ tokenizer,
+ system_prompt: str,
+ max_turns: int,
+ max_total_tokens: int,
+ sampling: SamplingCfg,
+ log_episode: Callable[[Task, list[dict]], None],
+) -> Callable:
+ """Build the closure GRPO calls each step.
+
+ ``prompts`` length equals ``num_generations``. We ignore the prompt strings
+ because the curriculum drives task selection — every rollout in the group
+ runs the same task forced through ``env.reset(task=...)``.
+ """
+
+ def rollout_func(prompts: list[str], trainer: GRPOTrainer) -> dict[str, list]:
+ task = curriculum.next_task()
+ results = pool.run_group(
+ task,
+ model,
+ tokenizer,
+ system_prompt,
+ max_turns,
+ max_total_tokens,
+ sampling,
+ )
+ # Pad / truncate to len(prompts) — defence in depth, group size should match.
+ if len(results) < len(prompts):
+ results.extend(results[-1:] * (len(prompts) - len(results)))
+ results = results[: len(prompts)]
+
+ group_rewards = [r["task_reward"] for r in results]
+ group_achieved = [r["task_achieved"] for r in results]
+ group_progress = [r["final_progress"] for r in results]
+
+ curriculum.record_result(
+ task,
+ achieved=any(group_achieved),
+ reward=float(sum(group_rewards) / len(group_rewards)) if group_rewards else 0.0,
+ )
+ log_episode(task, results)
+
+ return {
+ "prompt_ids": [r["prompt_ids"] for r in results],
+ "completion_ids": [r["completion_ids"] for r in results],
+ "logprobs": [r["logprobs"] for r in results],
+ "task_reward": group_rewards,
+ "task_achieved": [float(a) for a in group_achieved],
+ "final_progress": group_progress,
+ }
+
+ return rollout_func
+
+
+# ============================================================
+# CSV / JSONL logging + reward plotter
+# ============================================================
+
+
+class EpisodeLogger:
+ """Append-only CSV + JSONL writer for per-rollout episode rows."""
+
+ HEADER = [
+ "step",
+ "rollout_idx",
+ "task_id",
+ "difficulty",
+ "task_reward",
+ "task_achieved",
+ "final_progress",
+ "num_steps",
+ "tier",
+ "tier_success_rate",
+ "timestamp",
+ ]
+
+ def __init__(self, output_dir: Path) -> None:
+ self.output_dir = output_dir
+ output_dir.mkdir(parents=True, exist_ok=True)
+ self.csv_path = output_dir / "reward_log.csv"
+ self.jsonl_path = output_dir / "transcripts.jsonl"
+ if not self.csv_path.exists():
+ with open(self.csv_path, "w", newline="") as f:
+ csv.writer(f).writerow(self.HEADER)
+ self._step_counter = 0
+
+ def log(self, task: Task, results: list[dict], curriculum: Curriculum) -> None:
+ self._step_counter += 1
+ stats = curriculum.get_stats()
+ ts = datetime.now().isoformat()
+ with open(self.csv_path, "a", newline="") as f:
+ writer = csv.writer(f)
+ for i, r in enumerate(results):
+ writer.writerow(
+ [
+ self._step_counter,
+ i,
+ int(task.task_id),
+ task.difficulty.value,
+ f"{r['task_reward']:.4f}",
+ int(bool(r["task_achieved"])),
+ f"{r['final_progress']:.4f}",
+ r["num_steps"],
+ stats["tier"],
+ stats["tier_success_rate"],
+ ts,
+ ]
+ )
+ with open(self.jsonl_path, "a") as f:
+ for i, r in enumerate(results):
+ f.write(
+ json.dumps(
+ {
+ "step": self._step_counter,
+ "rollout_idx": i,
+ "task_id": int(task.task_id),
+ "difficulty": task.difficulty.value,
+ "task_reward": r["task_reward"],
+ "task_achieved": bool(r["task_achieved"]),
+ "final_progress": r["final_progress"],
+ "num_steps": r["num_steps"],
+ "tier": stats["tier"],
+ "transcript": r["transcript"],
+ }
+ )
+ + "\n"
+ )
+
+ rewards = [r["task_reward"] for r in results]
+ achieved = [bool(r["task_achieved"]) for r in results]
+ logger.info(
+ "Step %d task=%d (%s) rewards=%s achieved=%d/%d tier=%s tier_rate=%.2f",
+ self._step_counter,
+ int(task.task_id),
+ task.difficulty.value,
+ [round(r, 2) for r in rewards],
+ sum(achieved),
+ len(achieved),
+ stats["tier"],
+ stats["tier_success_rate"],
+ )
+
+
+def plot_rewards(csv_path: Path, out_path: Path) -> None:
+ """Per-step mean group reward + 10-step rolling avg + per-tier curves."""
+ import matplotlib
+
+ matplotlib.use("Agg")
+ import matplotlib.pyplot as plt
+
+ if not csv_path.exists():
+ logger.warning("No CSV at %s — skipping plot.", csv_path)
+ return
+
+ steps_data: dict[int, list[float]] = {}
+ tier_data: dict[str, list[tuple[int, float]]] = {}
+ with open(csv_path) as f:
+ reader = csv.DictReader(f)
+ for row in reader:
+ step = int(row["step"])
+ r = float(row["task_reward"])
+ tier = row["tier"]
+ steps_data.setdefault(step, []).append(r)
+ tier_data.setdefault(tier, []).append((step, r))
+
+ if not steps_data:
+ logger.warning("CSV at %s has no rows — skipping plot.", csv_path)
+ return
+
+ steps = sorted(steps_data.keys())
+ means = [sum(steps_data[s]) / len(steps_data[s]) for s in steps]
+
+ rolling = []
+ window = 10
+ for i in range(len(means)):
+ lo = max(0, i - window + 1)
+ rolling.append(sum(means[lo : i + 1]) / (i - lo + 1))
+
+ fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
+ ax1.plot(steps, means, label="mean group reward", alpha=0.5)
+ ax1.plot(steps, rolling, label=f"rolling avg (k={window})", linewidth=2)
+ ax1.set_xlabel("GRPO step")
+ ax1.set_ylabel("reward")
+ ax1.set_title("Group mean reward over training")
+ ax1.legend()
+ ax1.grid(alpha=0.3)
+
+ for tier, points in tier_data.items():
+ xs = [p[0] for p in points]
+ ys = [p[1] for p in points]
+ ax2.scatter(xs, ys, s=10, alpha=0.5, label=tier)
+ ax2.set_xlabel("GRPO step")
+ ax2.set_ylabel("reward")
+ ax2.set_title("Per-rollout reward by curriculum tier")
+ ax2.legend()
+ ax2.grid(alpha=0.3)
+
+ fig.tight_layout()
+ fig.savefig(out_path, dpi=120)
+ plt.close(fig)
+ logger.info("Reward plot written to %s", out_path)
+
+
+# ============================================================
+# Validation eval + Optuna search
+# ============================================================
+
+
+def pick_validation_task_ids(
+ curriculum: Optional[Curriculum] = None,
+ k_per_tier: int = 2,
+ seed: int = 42,
+) -> list[int]:
+ """Pick a frozen list of task ids — k per tier — for held-out validation."""
+ import random
+
+ rng = random.Random(seed)
+ cur = curriculum or Curriculum()
+ chosen: list[int] = []
+ for tier in TaskDifficulty:
+ try:
+ from server.services.curriculum import load_tier
+
+ tier_tasks = load_tier(tier, cur._tasks_dir)
+ except Exception as e:
+ logger.warning("Could not load tier %s for val: %s", tier.value, e)
+ continue
+ if not tier_tasks:
+ continue
+ sample = rng.sample(tier_tasks, k=min(k_per_tier, len(tier_tasks)))
+ chosen.extend(int(t.task_id) for t in sample)
+ return chosen
+
+
+def evaluate_on_validation(
+ model,
+ tokenizer,
+ pool: MultiTurnEnvPool,
+ val_task_ids: list[int],
+ system_prompt: str,
+ max_turns: int,
+ max_total_tokens: int,
+ sampling: SamplingCfg,
+ curriculum: Optional[Curriculum] = None,
+) -> dict[str, float]:
+ """Run ONE rollout per val task on env[0] of the pool. Return aggregate metrics."""
+ cur = curriculum or Curriculum()
+ achieved_flags: list[float] = []
+ progresses: list[float] = []
+ rewards: list[float] = []
+
+ async def _eval_one(task: Task) -> dict:
+ env = pool._envs[0]
+ return await rollout_one_episode(
+ env,
+ task,
+ model,
+ tokenizer,
+ system_prompt,
+ max_turns,
+ max_total_tokens,
+ sampling,
+ )
+
+ for tid in val_task_ids:
+ try:
+ task = cur.get_task_by_id(TaskID(int(tid)))
+ except KeyError:
+ logger.warning("val task_id=%d not found — skipping", tid)
+ continue
+ fut = asyncio.run_coroutine_threadsafe(_eval_one(task), pool._loop)
+ try:
+ res = fut.result(timeout=pool.timeout_s * max(1, max_turns))
+ except Exception as e:
+ logger.warning("val rollout failed for task=%d: %s", tid, e)
+ continue
+ achieved_flags.append(float(res["task_achieved"]))
+ progresses.append(float(res["final_progress"]))
+ rewards.append(float(res["task_reward"]))
+
+ n = max(1, len(achieved_flags))
+ return {
+ "achieved_rate": sum(achieved_flags) / n,
+ "mean_progress": sum(progresses) / n,
+ "mean_reward": sum(rewards) / n,
+ "n_evaluated": float(len(achieved_flags)),
+ }
+
+
+def _build_grpo_config(
+ output_dir: Path,
+ cfg: dict[str, Any],
+ max_steps: int,
+ max_completion_length: int,
+ max_prompt_length: int,
+ save_steps: int = 25,
+ save_strategy: str = "steps",
+ report_to: str = "none",
+) -> GRPOConfig:
+ return GRPOConfig(
+ output_dir=str(output_dir),
+ max_steps=max_steps,
+ learning_rate=float(cfg["learning_rate"]),
+ beta=float(cfg["beta"]),
+ num_generations=int(cfg["num_generations"]),
+ generation_batch_size=int(cfg["num_generations"]),
+ per_device_train_batch_size=1,
+ gradient_accumulation_steps=8,
+ gradient_checkpointing=True,
+ gradient_checkpointing_kwargs={"use_reentrant": False},
+ max_completion_length=max_completion_length,
+ max_prompt_length=max_prompt_length,
+ temperature=float(cfg["temperature"]),
+ top_p=float(cfg["top_p"]),
+ logging_steps=1,
+ save_strategy=save_strategy,
+ save_steps=save_steps,
+ save_total_limit=3,
+ report_to=report_to,
+ loss_type="dapo",
+ mask_truncated_completions=True,
+ warmup_ratio=0.05,
+ lr_scheduler_type="cosine",
+ max_grad_norm=1.0,
+ use_vllm=False,
+ remove_unused_columns=False,
+ )
+
+
+def _build_dummy_dataset(num_rows: int) -> Dataset:
+ """A length-only dataset; the prompts are ignored by ``rollout_func``."""
+ return Dataset.from_dict({"prompt": ["solve"] * max(1, num_rows)})
+
+
+def optuna_search(
+ n_trials: int,
+ trial_max_steps: int,
+ val_task_ids: list[int],
+ base_model: str,
+ sft_adapter: Optional[str],
+ env_url: str,
+ output_dir: Path,
+ max_total_tokens: int = 2048,
+ max_completion_length: int = 256,
+ max_prompt_length: int = 2048,
+ seed: int = 42,
+):
+ """TPE-sampled hyperparam search. Persists to ``output_dir/optuna.db``."""
+ import optuna
+
+ output_dir.mkdir(parents=True, exist_ok=True)
+ study = optuna.create_study(
+ direction="maximize",
+ study_name="aws-rl-grpo",
+ storage=f"sqlite:///{output_dir / 'optuna.db'}",
+ load_if_exists=True,
+ sampler=optuna.samplers.TPESampler(seed=seed),
+ )
+
+ def _objective(trial: optuna.Trial) -> float:
+ cfg = {
+ "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True),
+ "beta": trial.suggest_float("beta", 0.0, 0.1),
+ "num_generations": trial.suggest_categorical("num_generations", [4, 8]),
+ "temperature": trial.suggest_float("temperature", 0.7, 1.0),
+ "top_p": trial.suggest_float("top_p", 0.85, 0.98),
+ "lora_r": trial.suggest_categorical("lora_r", [8, 16, 32]),
+ "lora_alpha_mul": trial.suggest_categorical("lora_alpha_mul", [1, 2, 4]),
+ "max_turns": trial.suggest_categorical("max_turns", [4, 6, 8]),
+ }
+ trial_dir = output_dir / f"trial_{trial.number:03d}"
+ return _run_one_trial(
+ cfg=cfg,
+ trial_max_steps=trial_max_steps,
+ val_task_ids=val_task_ids,
+ base_model=base_model,
+ sft_adapter=sft_adapter,
+ env_url=env_url,
+ output_dir=trial_dir,
+ max_total_tokens=max_total_tokens,
+ max_completion_length=max_completion_length,
+ max_prompt_length=max_prompt_length,
+ )
+
+ study.optimize(_objective, n_trials=n_trials, gc_after_trial=True)
+
+ best_path = output_dir / "best_cfg.json"
+ payload = {"best_value": study.best_value, "best_params": dict(study.best_params)}
+ with open(best_path, "w") as f:
+ json.dump(payload, f, indent=2)
+ logger.info(
+ "Optuna study finished. best_value=%.4f best_params=%s -> %s",
+ study.best_value,
+ study.best_params,
+ best_path,
+ )
+ return study
+
+
+def _run_one_trial(
+ cfg: dict[str, Any],
+ trial_max_steps: int,
+ val_task_ids: list[int],
+ base_model: str,
+ sft_adapter: Optional[str],
+ env_url: str,
+ output_dir: Path,
+ max_total_tokens: int,
+ max_completion_length: int,
+ max_prompt_length: int,
+) -> float:
+ """One Optuna trial: load → train → eval on val tasks → tear down → return objective."""
+ output_dir.mkdir(parents=True, exist_ok=True)
+ logger.info("Optuna trial cfg=%s -> %s", cfg, output_dir)
+
+ model = tokenizer = None
+ pool: Optional[MultiTurnEnvPool] = None
+ trainer: Optional[GRPOTrainer] = None
+ try:
+ model, tokenizer = load_policy(base_model, sft_adapter, trainable=True)
+
+ pool = MultiTurnEnvPool(env_url, size=int(cfg["num_generations"]))
+ pool.start()
+
+ curriculum = Curriculum()
+ sampling = SamplingCfg(
+ temperature=float(cfg["temperature"]),
+ top_p=float(cfg["top_p"]),
+ max_new_tokens=max_completion_length,
+ max_prompt_length=max_prompt_length,
+ )
+ ep_logger = EpisodeLogger(output_dir)
+ rollout_func = make_rollout_func(
+ curriculum=curriculum,
+ pool=pool,
+ model=model,
+ tokenizer=tokenizer,
+ system_prompt=SYSTEM_PROMPT,
+ max_turns=int(cfg["max_turns"]),
+ max_total_tokens=max_total_tokens,
+ sampling=sampling,
+ log_episode=lambda task, results: ep_logger.log(task, results, curriculum),
+ )
+
+ dataset = _build_dummy_dataset(trial_max_steps * int(cfg["num_generations"]))
+ grpo_cfg = _build_grpo_config(
+ output_dir=output_dir,
+ cfg=cfg,
+ max_steps=trial_max_steps,
+ max_completion_length=max_completion_length,
+ max_prompt_length=max_prompt_length,
+ save_strategy="no",
+ report_to="none",
+ )
+
+ trainer = GRPOTrainer(
+ model=model,
+ processing_class=tokenizer,
+ reward_funcs=[reward_task, reward_achieved, reward_progress],
+ train_dataset=dataset,
+ args=grpo_cfg,
+ rollout_func=rollout_func,
+ peft_config=None if sft_adapter else _lora_config(cfg),
+ )
+ trainer.train()
+
+ metrics = evaluate_on_validation(
+ model=trainer.model,
+ tokenizer=tokenizer,
+ pool=pool,
+ val_task_ids=val_task_ids,
+ system_prompt=SYSTEM_PROMPT,
+ max_turns=int(cfg["max_turns"]),
+ max_total_tokens=max_total_tokens,
+ sampling=sampling,
+ curriculum=curriculum,
+ )
+ objective = 0.7 * metrics["achieved_rate"] + 0.3 * metrics["mean_progress"]
+ with open(output_dir / "trial_metrics.json", "w") as f:
+ json.dump({"cfg": cfg, "metrics": metrics, "objective": objective}, f, indent=2)
+ logger.info("Trial done: metrics=%s objective=%.4f", metrics, objective)
+ return float(objective)
+ finally:
+ if trainer is not None:
+ try:
+ del trainer
+ except Exception:
+ pass
+ if model is not None:
+ free_model(model)
+ if pool is not None:
+ try:
+ pool.close()
+ except Exception:
+ logger.exception("Pool close error during trial cleanup")
+ gc.collect()
+ if torch.cuda.is_available():
+ torch.cuda.empty_cache()
+
+
+def _lora_config(cfg: dict[str, Any]) -> LoraConfig:
+ r = int(cfg["lora_r"])
+ alpha_mul = int(cfg["lora_alpha_mul"])
+ return LoraConfig(
+ r=r,
+ lora_alpha=r * alpha_mul,
+ lora_dropout=0.05,
+ bias="none",
+ task_type="CAUSAL_LM",
+ target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+ )
+
+
+# ============================================================
+# Main training entrypoint (single training pass)
+# ============================================================
+
+
+def run_training(
+ cfg: dict[str, Any],
+ *,
+ base_model: str,
+ sft_adapter: Optional[str],
+ env_url: str,
+ output_dir: Path,
+ max_steps: int,
+ max_total_tokens: int = 4096,
+ max_completion_length: int = 256,
+ max_prompt_length: int = 2048,
+ push_to_hub: bool = False,
+ hub_repo: Optional[str] = None,
+) -> Path:
+ """Run a full GRPO training pass with the supplied config dict."""
+ output_dir.mkdir(parents=True, exist_ok=True)
+ logger.info("run_training cfg=%s -> %s", cfg, output_dir)
+
+ model, tokenizer = load_policy(base_model, sft_adapter, trainable=True)
+ pool = MultiTurnEnvPool(env_url, size=int(cfg["num_generations"]))
+ pool.start()
+
+ curriculum = Curriculum()
+ sampling = SamplingCfg(
+ temperature=float(cfg["temperature"]),
+ top_p=float(cfg["top_p"]),
+ max_new_tokens=max_completion_length,
+ max_prompt_length=max_prompt_length,
+ )
+ ep_logger = EpisodeLogger(output_dir)
+ rollout_func = make_rollout_func(
+ curriculum=curriculum,
+ pool=pool,
+ model=model,
+ tokenizer=tokenizer,
+ system_prompt=SYSTEM_PROMPT,
+ max_turns=int(cfg["max_turns"]),
+ max_total_tokens=max_total_tokens,
+ sampling=sampling,
+ log_episode=lambda task, results: ep_logger.log(task, results, curriculum),
+ )
+
+ dataset = _build_dummy_dataset(max_steps * int(cfg["num_generations"]))
+ grpo_cfg = _build_grpo_config(
+ output_dir=output_dir,
+ cfg=cfg,
+ max_steps=max_steps,
+ max_completion_length=max_completion_length,
+ max_prompt_length=max_prompt_length,
+ )
+
+ trainer = GRPOTrainer(
+ model=model,
+ processing_class=tokenizer,
+ reward_funcs=[reward_task, reward_achieved, reward_progress],
+ train_dataset=dataset,
+ args=grpo_cfg,
+ rollout_func=rollout_func,
+ peft_config=None if sft_adapter else _lora_config(cfg),
+ )
+
+ try:
+ trainer.train()
+ finally:
+ try:
+ pool.close()
+ except Exception:
+ logger.exception("Pool close error after training")
+ try:
+ plot_rewards(ep_logger.csv_path, output_dir / "reward_plot.png")
+ except Exception as e:
+ logger.warning("plot_rewards failed: %s", e)
+
+ trainer.save_model(str(output_dir))
+ logger.info("Adapter saved to %s", output_dir)
+
+ if push_to_hub and hub_repo:
+ trainer.push_to_hub(repo_id=hub_repo)
+ logger.info("Adapter pushed to https://huggingface.co/%s", hub_repo)
+
+ return output_dir
+
+
+# ============================================================
+# CLI
+# ============================================================
+
+
+def _parse_args() -> argparse.Namespace:
+ p = argparse.ArgumentParser(description=__doc__)
+ p.add_argument("--mode", choices=["train", "optuna", "full"], default="train")
+ p.add_argument("--base-model", default=PolicySpec.base_model)
+ p.add_argument("--sft-adapter", default=PolicySpec.sft_adapter,
+ help="HF repo id of the SFT adapter (use empty string to disable)")
+ p.add_argument("--env-url", default="http://localhost:8000")
+ p.add_argument("--output-dir", default=None)
+
+ # Train-mode hyperparams (mirror DEFAULT_CFG keys)
+ p.add_argument("--num-generations", type=int, default=DEFAULT_CFG["num_generations"])
+ p.add_argument("--max-turns", type=int, default=DEFAULT_CFG["max_turns"])
+ p.add_argument("--max-steps", type=int, default=200)
+ p.add_argument("--learning-rate", type=float, default=DEFAULT_CFG["learning_rate"])
+ p.add_argument("--beta", type=float, default=DEFAULT_CFG["beta"])
+ p.add_argument("--temperature", type=float, default=DEFAULT_CFG["temperature"])
+ p.add_argument("--top-p", type=float, default=DEFAULT_CFG["top_p"])
+ p.add_argument("--lora-r", type=int, default=DEFAULT_CFG["lora_r"])
+ p.add_argument("--lora-alpha-mul", type=int, default=DEFAULT_CFG["lora_alpha_mul"])
+ p.add_argument("--max-prompt-length", type=int, default=2048)
+ p.add_argument("--max-completion-length", type=int, default=256)
+ p.add_argument("--max-total-tokens", type=int, default=4096)
+
+ # Optuna-specific
+ p.add_argument("--n-trials", type=int, default=6)
+ p.add_argument("--trial-max-steps", type=int, default=30)
+ p.add_argument("--val-tasks-per-tier", type=int, default=2)
+
+ p.add_argument("--push-to-hub", action="store_true")
+ p.add_argument("--hub-repo", default=None)
+ return p.parse_args()
+
+
+def _resolve_output_dir(args: argparse.Namespace) -> Path:
+ if args.output_dir:
+ return Path(args.output_dir)
+ ts = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+ return Path("outputs") / f"aws-rl-grpo-{ts}"
+
+
+def _cli_cfg(args: argparse.Namespace) -> dict[str, Any]:
+ return {
+ "learning_rate": args.learning_rate,
+ "beta": args.beta,
+ "num_generations": args.num_generations,
+ "temperature": args.temperature,
+ "top_p": args.top_p,
+ "lora_r": args.lora_r,
+ "lora_alpha_mul": args.lora_alpha_mul,
+ "max_turns": args.max_turns,
+ }
+
+
+def main() -> None:
+ logging.basicConfig(
+ level=logging.INFO,
+ format="%(asctime)s %(levelname)s %(name)s %(message)s",
+ )
+ args = _parse_args()
+ output_dir = _resolve_output_dir(args)
+ output_dir.mkdir(parents=True, exist_ok=True)
+ sft_adapter = args.sft_adapter or None
+
+ if args.mode in ("optuna", "full"):
+ val_ids = pick_validation_task_ids(k_per_tier=args.val_tasks_per_tier)
+ with open(output_dir / "val_task_ids.json", "w") as f:
+ json.dump(val_ids, f)
+ study = optuna_search(
+ n_trials=args.n_trials,
+ trial_max_steps=args.trial_max_steps,
+ val_task_ids=val_ids,
+ base_model=args.base_model,
+ sft_adapter=sft_adapter,
+ env_url=args.env_url,
+ output_dir=output_dir,
+ max_total_tokens=args.max_total_tokens,
+ max_completion_length=args.max_completion_length,
+ max_prompt_length=args.max_prompt_length,
+ )
+ if args.mode == "optuna":
+ return
+ cfg = {**DEFAULT_CFG, **dict(study.best_params)}
+ else:
+ cfg = _cli_cfg(args)
+
+ run_training(
+ cfg,
+ base_model=args.base_model,
+ sft_adapter=sft_adapter,
+ env_url=args.env_url,
+ output_dir=output_dir,
+ max_steps=args.max_steps,
+ max_total_tokens=args.max_total_tokens,
+ max_completion_length=args.max_completion_length,
+ max_prompt_length=args.max_prompt_length,
+ push_to_hub=args.push_to_hub,
+ hub_repo=args.hub_repo,
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/train_grpo_lora_final.ipynb b/train_grpo_lora_final.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..5d1f937809a6d1a0b8720d844ddcc833fb20bc72
--- /dev/null
+++ b/train_grpo_lora_final.ipynb
@@ -0,0 +1,16195 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "c108b1a2",
+ "metadata": {
+ "id": "c108b1a2"
+ },
+ "source": [
+ "# GRPO Fine-Tuning on the AWS RL Environment\n",
+ "\n",
+ "**Curriculum-driven GRPO**, built on top of the existing SFT LoRA adapter ([`Sizzing/aws-rl-sft-qwen25coder3b-adapter`](https://huggingface.co/Sizzing/aws-rl-sft-qwen25coder3b-adapter)). The priority-queue curriculum in [`server/services/curriculum.py`](https://github.com/UdayKiranPadhy/aws-rl-env/blob/master/server/services/curriculum.py) picks each training task based on novelty, weakness, and spaced repetition; TRL's `GRPOTrainer` owns the generate/score/update loop; rewards flow back into the curriculum through the reward function to close the mastery loop.\n",
+ "\n",
+ "Hyperparameter-tuned with Optuna, logged to Weights & Biases, checkpointed for safe resume, and published to a **separate** HuggingFace Hub repo so both the SFT and GRPO adapters coexist side-by-side.\n",
+ "\n",
+ "### Architecture\n",
+ "```\n",
+ "Hosted env server (Docker, AWS_RL_ENV_POOL_SIZE=8)\n",
+ " ▲ HTTPS tunnel\n",
+ " │ Colab / Kaggle VM (T4)\n",
+ " │ └─ main python\n",
+ " │ 8-way httpx pool (rewards) ├─ Unsloth: base + SFT adapter (trainable)\n",
+ " ├──────────────────────── │\n",
+ " │ ├─ TRL GRPOTrainer\n",
+ " │ 8-way GrpoPool WS (eval) │ ├─ train_ds = stream from Curriculum\n",
+ " └──────────────────────── │ ├─ G generations / prompt\n",
+ " │ ├─ reward_fn:\n",
+ " │ │ a) score via /reset + /step\n",
+ " │ │ b) curriculum.record_result(...)\n",
+ " │ └─ group-advantage + PPO-clip on LoRA\n",
+ " ├─ Optuna (sqlite, resumable)\n",
+ " └─ push → Sizzing/aws-rl-grpo-qwen25coder3b-adapter\n",
+ "```\n",
+ "\n",
+ "### Why curriculum-driven GRPO?\n",
+ "Plain dataset iteration is agnostic to the model’s current weaknesses. The curriculum surfaces novel + weak tasks more often, spaced-repeats mastered ones, and promotes tier only when the agent demonstrates sustained competence. GRPO's group-relative advantage + the curriculum's per-task mastery signal compose: a group of G completions on a hard task yields a high-variance advantage signal exactly when it matters.\n",
+ "\n",
+ "### Headline metrics (reported in the eval cells)\n",
+ "| Axis | SFT baseline | GRPO target | Stretch |\n",
+ "|----------------------|-------------:|------------:|--------:|\n",
+ "| `env_reward_mean` | ~0.85 | ≥0.92 | ≥0.97 |\n",
+ "| `env_success_rate` | ~85% | ≥92% | ≥97% |\n",
+ "| `recovery_rate` | ~40% | ≥60% | ≥75% |\n",
+ "| `drift_repair_rate` | ~30% | ≥55% | ≥70% |\n",
+ "| `hints_per_solved` | ~0.8 | ≤0.5 | ≤0.2 |\n",
+ "| `steps_to_solve` | ~6 | ≤5 | ≤4 |"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3f7aa285",
+ "metadata": {
+ "id": "3f7aa285"
+ },
+ "source": [
+ "## 1 · Install dependencies\n",
+ "\n",
+ "Mirrors the SFT notebook's pinning strategy. Key constraints: `trl>=0.16` for `GRPOTrainer`, `transformers>=4.50,<5.0` for Unsloth compatibility (the SFT notebook ran into this too), `--force-reinstall --no-deps` on `transformers` so TRL doesn't pull an incompatible version."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "2f683e16",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2f683e16",
+ "outputId": "cadbf031-23d4-4b43-fc9a-a59aa5dc7103"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.8 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m68.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "%pip install -q --upgrade pip\n",
+ "%pip install -q \"unsloth\"\n",
+ "%pip install -q \"trl>=0.18.2,<=0.24.0,!=0.19.0\" \"peft\" \"accelerate\" \"datasets\" \"bitsandbytes\"\n",
+ "%pip install -q \"transformers>=4.50,<5.0\" --force-reinstall --no-deps\n",
+ "%pip install -q \"optuna\"\n",
+ "%pip install -q \"httpx\" \"websockets\" \"pyyaml\" \"python-dotenv\"\n",
+ "%pip install -q \"huggingface_hub>=0.34,<1.0\"\n",
+ "%pip install -q \"openenv-core\"\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "logging-setup-md",
+ "metadata": {
+ "id": "logging-setup-md"
+ },
+ "source": [
+ "### 1b · Logging & warning suppression\n",
+ "\n",
+ "Set up structured logging and suppress known benign warnings (PEFT config keys, notebook escape sequences, transformers do_sample advisory)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "logging-setup",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "logging-setup",
+ "outputId": "8857f297-9d17-4e62-e663-af002807003f"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14:52:11 | INFO | grpo | Logging configured; benign warnings suppressed.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import logging\n",
+ "import sys\n",
+ "import warnings\n",
+ "\n",
+ "# ── Structured logging ──────────────────────────────────────────────\n",
+ "logging.basicConfig(\n",
+ " level=logging.INFO,\n",
+ " format=\"%(asctime)s | %(levelname)-7s | %(name)s | %(message)s\",\n",
+ " datefmt=\"%H:%M:%S\",\n",
+ " stream=sys.stdout,\n",
+ " force=True,\n",
+ ")\n",
+ "log = logging.getLogger(\"grpo\")\n",
+ "\n",
+ "# ── Suppress known benign warnings ──────────────────────────────────\n",
+ "# PEFT adapter was saved with a newer version; extra keys are safely ignored.\n",
+ "warnings.filterwarnings(\n",
+ " \"ignore\",\n",
+ " message=r\"Unexpected keyword arguments.*for class LoraConfig\",\n",
+ " category=UserWarning,\n",
+ ")\n",
+ "# The notebook package itself has an invalid escape sequence in its banner.\n",
+ "warnings.filterwarnings(\n",
+ " \"ignore\",\n",
+ " message=r\"invalid escape sequence\",\n",
+ " category=SyntaxWarning,\n",
+ ")\n",
+ "# Transformers logs \"do_sample is False but temperature is set\" when we\n",
+ "# intentionally use greedy decoding with temperature=1.0 (the default).\n",
+ "warnings.filterwarnings(\n",
+ " \"ignore\",\n",
+ " message=r\".*`do_sample` is set to `False`.*`temperature`.*\",\n",
+ " category=UserWarning,\n",
+ ")\n",
+ "\n",
+ "log.info(\"Logging configured; benign warnings suppressed.\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ce043e2c",
+ "metadata": {
+ "id": "ce043e2c"
+ },
+ "source": [
+ "## 2 · Clone the AWS RL env repo\n",
+ "\n",
+ "We need the `client.AwsRlEnv` class, `models.py` pydantic types, `scripts/grpo_pool.py`, the curriculum loader, and the task YAMLs — these power the multi-step evaluator. We do **not** install or run any env server locally; the env is hosted elsewhere."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "2c0842fb",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2c0842fb",
+ "outputId": "5ecf6f16-713b-4d20-bfeb-1450798b90c9"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14:52:16 | INFO | grpo | Repo at /content/aws-rl-env — ready. sys.path[0]=/content/aws-rl-env\n"
+ ]
+ }
+ ],
+ "source": [
+ "from __future__ import annotations\n",
+ "import os, sys, subprocess, shutil\n",
+ "from pathlib import Path\n",
+ "\n",
+ "REPO_URL = \"https://github.com/UdayKiranPadhy/aws-rl-env.git\"\n",
+ "\n",
+ "# Detect host in priority order: Kaggle first (it often has /content too),\n",
+ "# then Colab, then fall back to CWD for local runs.\n",
+ "if Path(\"/kaggle/working\").exists():\n",
+ " REPO_DIR = Path(\"/kaggle/working/aws-rl-env\")\n",
+ "elif Path(\"/content\").exists():\n",
+ " REPO_DIR = Path(\"/content/aws-rl-env\")\n",
+ "else:\n",
+ " REPO_DIR = (Path.cwd() / \"aws-rl-env\").resolve()\n",
+ "\n",
+ "if not REPO_DIR.exists():\n",
+ " subprocess.run([\"git\", \"clone\", \"--depth\", \"1\", REPO_URL, str(REPO_DIR)], check=True)\n",
+ "\n",
+ "# If a prior partial clone left no models.py, redo it.\n",
+ "if not (REPO_DIR / \"models.py\").exists():\n",
+ " shutil.rmtree(REPO_DIR, ignore_errors=True)\n",
+ " subprocess.run([\"git\", \"clone\", \"--depth\", \"1\", REPO_URL, str(REPO_DIR)], check=True)\n",
+ "\n",
+ "if str(REPO_DIR) not in sys.path:\n",
+ " sys.path.insert(0, str(REPO_DIR))\n",
+ "\n",
+ "assert (REPO_DIR / \"models.py\").exists(), f\"models.py missing under {REPO_DIR}\"\n",
+ "log.info(\"Repo at %s — ready. sys.path[0]=%s\", REPO_DIR, sys.path[0])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "253e6837",
+ "metadata": {
+ "id": "253e6837"
+ },
+ "source": [
+ "## 3 · Runtime detection\n",
+ "\n",
+ "Matches the SFT notebook so we pick `fp16` on T4 (bf16 unsupported) and `bf16` on A100/H100. Also sets the PyTorch allocator env var that cut OOMs on long Unsloth runs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "cf5059b1",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "cf5059b1",
+ "outputId": "a5d04464-de76-4ccd-ef8b-02ac996bb034"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14:52:29 | INFO | grpo | GPU: Tesla T4 | fp16=True bf16=False | OUT_DIR=/content/out\n"
+ ]
+ }
+ ],
+ "source": [
+ "from dataclasses import dataclass\n",
+ "import torch\n",
+ "\n",
+ "IS_COLAB = True\n",
+ "IS_KAGGLE = False\n",
+ "\n",
+ "OUT_DIR = Path(\"/content/out\") if IS_COLAB else Path(\"/kaggle/working\") if IS_KAGGLE else Path.cwd() / \"out\"\n",
+ "OUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "os.environ.setdefault(\"PYTORCH_ALLOC_CONF\", \"expandable_segments:True\")\n",
+ "\n",
+ "\n",
+ "@dataclass(frozen=True)\n",
+ "class Runtime:\n",
+ " gpu_name: str\n",
+ " use_fp16: bool\n",
+ " use_bf16: bool\n",
+ "\n",
+ "\n",
+ "def detect_runtime() -> Runtime:\n",
+ " if not torch.cuda.is_available():\n",
+ " raise RuntimeError(\"No GPU detected. This notebook needs CUDA (T4/A100/H100).\")\n",
+ " name = torch.cuda.get_device_name(0)\n",
+ " is_t4 = \"T4\" in name\n",
+ " return Runtime(gpu_name=name, use_fp16=is_t4, use_bf16=not is_t4)\n",
+ "\n",
+ "\n",
+ "RT = detect_runtime()\n",
+ "log.info(\"GPU: %s | fp16=%s bf16=%s | OUT_DIR=%s\", RT.gpu_name, RT.use_fp16, RT.use_bf16, OUT_DIR)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "93c46602",
+ "metadata": {
+ "id": "93c46602"
+ },
+ "source": [
+ "## 4 · Training configuration\n",
+ "\n",
+ "One frozen `TrainingConfig` dataclass holds every tunable hyperparameter. Optuna hands trials a mutated copy of this same dataclass, so the trial path and the final-run path go through identical code.\n",
+ "\n",
+ "Separate `ModelSpec` and `PathsSpec` keep non-tunable identifiers out of the search space."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "59787b12",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "59787b12",
+ "outputId": "eded1e8f-7933-4625-fdde-fa3352cf8b0d"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14:52:32 | INFO | grpo | ModelSpec: ModelSpec(base_model='unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit', sft_adapter='Sizzing/aws-rl-sft-qwen25coder3b-adapter', grpo_adapter='Sizzing/aws-rl-grpo-qwen25coder3b-adapter', dataset_repo='Sizzing/aws-rl-sft', max_seq_length=3072)\n",
+ "14:52:32 | INFO | grpo | TrainingConfig defaults: TrainingConfig(learning_rate=5e-06, beta=0.04, num_generations=8, temperature=0.9, top_p=0.95, max_prompt_length=512, max_completion_length=768, per_device_train_batch_size=2, gradient_accumulation_steps=8, num_train_epochs=1, save_steps=25, save_total_limit=3, eval_steps=50, warmup_ratio=0.05, seed=42)\n",
+ "14:52:32 | INFO | grpo | PipelineConfig: PipelineConfig(env_pool_size=8, n_trials=4, trial_max_steps=10, final_max_steps=35, val_subset_size=20, eval_reserve_cap=100, optuna_tier_counts=(('warmup', 2), ('beginner', 2), ('intermediate', 2), ('advanced', 3), ('expert', 3)))\n"
+ ]
+ }
+ ],
+ "source": [
+ "from dataclasses import replace\n",
+ "\n",
+ "\n",
+ "@dataclass(frozen=True)\n",
+ "class ModelSpec:\n",
+ " base_model: str = \"unsloth/Qwen2.5-Coder-3B-Instruct-bnb-4bit\"\n",
+ " sft_adapter: str = \"Sizzing/aws-rl-sft-qwen25coder3b-adapter\"\n",
+ " grpo_adapter: str = \"Sizzing/aws-rl-grpo-qwen25coder3b-adapter\" # NEW repo — SFT repo untouched\n",
+ " dataset_repo: str = \"Sizzing/aws-rl-sft\"\n",
+ " max_seq_length: int = 3072\n",
+ "\n",
+ "\n",
+ "@dataclass(frozen=True)\n",
+ "class TrainingConfig:\n",
+ " # GRPO knobs (Optuna-tunable)\n",
+ " learning_rate: float = 5e-6 # AdamW learning rate;\n",
+ " beta: float = 0.04 # KL coef vs. reference (frozen SFT adapter)\n",
+ " num_generations: int = 8 # G in GRPO\n",
+ " temperature: float = 0.9 # Sampling temp during generation\n",
+ " top_p: float = 0.95 # Nucleus sampling cutoff\n",
+ " max_prompt_length: int = 512 # Hard truncate on the prompt side\n",
+ " max_completion_length: int = 768 # Max tokens on the completion side;\n",
+ " per_device_train_batch_size: int = 2 # Batch per GPU\n",
+ " gradient_accumulation_steps: int = 8 # \tEffective batch = pdtbs × grad_accum (= 16 now)\n",
+ " num_train_epochs: int = 1 # ignored for IterableDataset; max_steps drives termination\n",
+ " save_steps: int = 25 # How often the final run writes a checkpoint-N\n",
+ " save_total_limit: int = 3 # Max number of checkpoints to keep; older ones get deleted\n",
+ " eval_steps: int = 50 # How often to run evaluation\n",
+ " warmup_ratio: float = 0.05\n",
+ " seed: int = 42 # Seeds GRPOConfig + Optuna's TPESampler\n",
+ "\n",
+ "\n",
+ "@dataclass(frozen=True)\n",
+ "class PipelineConfig:\n",
+ " env_pool_size: int = 8\n",
+ " n_trials: int = 4 # Optuna trials for hyperparameter search; each trial trains a GRPO agent from the same SFT starting point, but with different hyperparameters.\n",
+ " trial_max_steps: int = 10 # max_steps for each Optuna trial; keeps trial runtime manageable and encourages faster feedback on hyperparameter choices. The final evaluation after all trials will use a longer max_steps to allow the best trial more time to shine.\n",
+ " final_max_steps: int = 35 # GRPO steps for the post-Optuna final run\n",
+ " val_subset_size: int = 20\n",
+ " eval_reserve_cap: int = 100\n",
+ " # 12-task pool used for both Optuna trial training and trial scoring.\n",
+ " # Tuple-of-tuples keeps the dataclass frozen/hashable.\n",
+ " optuna_tier_counts: tuple = (\n",
+ " (\"warmup\", 2), (\"beginner\", 2), (\"intermediate\", 2),\n",
+ " (\"advanced\", 3), (\"expert\", 3),\n",
+ " )\n",
+ "\n",
+ "\n",
+ "MODEL = ModelSpec()\n",
+ "TRAIN = TrainingConfig()\n",
+ "PIPE = PipelineConfig()\n",
+ "\n",
+ "log.info(\"ModelSpec: %s\", MODEL)\n",
+ "log.info(\"TrainingConfig defaults: %s\", TRAIN)\n",
+ "log.info(\"PipelineConfig: %s\", PIPE)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5dcff29c",
+ "metadata": {
+ "id": "5dcff29c"
+ },
+ "source": [
+ "## 5 · Authenticate: HF Hub + env URL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "e58946d9",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "e58946d9",
+ "outputId": "eb9d4b8b-05e3-4206-e14e-f170ed809a17"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "No files have been modified since last commit. Skipping to prevent empty commit.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14:52:38 | WARNING | huggingface_hub.hf_api | No files have been modified since last commit. Skipping to prevent empty commit.\n",
+ "14:52:38 | INFO | grpo | All secrets loaded; HF write access to Sizzing/aws-rl-grpo-qwen25coder3b-adapter verified.\n"
+ ]
+ }
+ ],
+ "source": [
+ "def _load_secret(name: str) -> str:\n",
+ " \"\"\"Read a secret from Colab userdata, Kaggle UserSecrets, or the env.\"\"\"\n",
+ " if IS_COLAB:\n",
+ " from google.colab import userdata\n",
+ " try: return userdata.get(name)\n",
+ " except Exception: pass\n",
+ " if IS_KAGGLE:\n",
+ " from kaggle_secrets import UserSecretsClient\n",
+ " try: return UserSecretsClient().get_secret(name)\n",
+ " except Exception: pass\n",
+ " val = os.environ.get(name)\n",
+ " if not val:\n",
+ " raise RuntimeError(f\"Secret {name!r} is missing. Set it in Colab/Kaggle secrets or as an env var.\")\n",
+ " return val\n",
+ "\n",
+ "\n",
+ "HF_TOKEN = _load_secret(\"HF_TOKEN\")\n",
+ "ENV_BASE_URL = _load_secret(\"AWS_RL_ENV_BASE_URL\").rstrip(\"/\")\n",
+ "\n",
+ "from huggingface_hub import login as hf_login, HfApi\n",
+ "\n",
+ "hf_login(token=HF_TOKEN, add_to_git_credential=False)\n",
+ "\n",
+ "\n",
+ "def verify_hub_write_scope(repo_id: str) -> None:\n",
+ " \"\"\"Ensure the HF token can create repos under the target org before training.\n",
+ "\n",
+ " Without this, we'd discover permission failures *after* a multi-hour run.\n",
+ " \"\"\"\n",
+ " api = HfApi(token=HF_TOKEN)\n",
+ " api.create_repo(repo_id=repo_id, private=True, exist_ok=True, repo_type=\"model\")\n",
+ " probe = OUT_DIR / \".hub_write_probe.txt\"\n",
+ " probe.write_text(\"ok\")\n",
+ " api.upload_file(path_or_fileobj=str(probe), path_in_repo=\".hub_write_probe.txt\",\n",
+ " repo_id=repo_id, commit_message=\"probe: verify write scope\")\n",
+ " probe.unlink()\n",
+ "\n",
+ "\n",
+ "verify_hub_write_scope(MODEL.grpo_adapter)\n",
+ "log.info(\"All secrets loaded; HF write access to %s verified.\", MODEL.grpo_adapter)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a6a47eb8",
+ "metadata": {
+ "id": "a6a47eb8"
+ },
+ "source": [
+ "## 6 · Connect to the hosted env + health check\n",
+ "\n",
+ "The env server runs **outside** this VM. We only assert reachability and that its internal pool has 8 slots (required for parallel rollouts). If either fails, we stop **before** loading the model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "e30369da",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "e30369da",
+ "outputId": "b9a5cfa8-eaee-4f64-c157-cd2b2b241ff5"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14:52:44 | INFO | httpx | HTTP Request: GET https://sizzing-aws-rl-env.hf.space/schema \"HTTP/1.1 200 OK\"\n",
+ "14:52:44 | INFO | grpo | Env reachable. Action schema keys: ['action', 'observation', 'state']\n",
+ "14:52:44 | INFO | grpo | POOL_SIZE=8 assumed — verified by GrpoPool connection below.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import httpx\n",
+ "\n",
+ "\n",
+ "def probe_env_http(base_url: str) -> dict:\n",
+ " \"\"\"Cheap reachability check: GET /schema. Raises on HTTP error.\n",
+ "\n",
+ " Does NOT try to validate pool size from /state — AwsRlState doesn't\n",
+ " expose it. Pool capacity is verified later via a real GrpoPool\n",
+ " connection attempt (§6b) which is the only honest way to check.\n",
+ " \"\"\"\n",
+ " with httpx.Client(base_url=base_url, timeout=10.0) as c:\n",
+ " schema = c.get(\"/schema\").raise_for_status().json()\n",
+ " return {\"schema\": schema}\n",
+ "\n",
+ "\n",
+ "probe = probe_env_http(ENV_BASE_URL)\n",
+ "log.info(\"Env reachable. Action schema keys: %s\", list(probe['schema'].keys()))\n",
+ "log.info(\"POOL_SIZE=%d assumed — verified by GrpoPool connection below.\", PIPE.env_pool_size)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b35c3272",
+ "metadata": {
+ "id": "b35c3272"
+ },
+ "source": [
+ "## 7 · Curriculum-driven prompt stream + fixed val / reserve sets\n",
+ "\n",
+ "Instead of feeding a static dataset, we stream prompts from the repo's [`Curriculum`](https://github.com/UdayKiranPadhy/aws-rl-env/blob/master/server/services/curriculum.py) — the same priority-queue curriculum the env already implements:\n",
+ "\n",
+ "- **novelty bonus** for untried tasks (+100)\n",
+ "- **weakness weighting** `(1 − recent_success_rate) × 50` per task\n",
+ "- **spaced repetition** on graduated tasks at 3→66→12→24→48 episode intervals\n",
+ "- **recency penalty** to avoid drilling the same task back-to-back\n",
+ "- **tier promotion** with fast-track when success rate crosses threshold\n",
+ "\n",
+ "TRL's `GRPOTrainer` accepts a `datasets.IterableDataset`; we wrap `curriculum.next_task()` in a generator and feed it in. The feedback loop closes inside the reward function (next cell): after scoring a group of G completions for a task, it calls `curriculum.record_result(task, achieved, mean_reward)`, which updates mastery, promotes tiers, and re-ranks the priority queue for the next step.\n",
+ "\n",
+ "`VAL_DS` (fixed 20-row subset from the HF dataset's `validation` split) and `RESERVE_DS` (up to 100 rows from `reserve`) stay fixed for comparable before/after evaluation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "0d3fe743",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 647,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "0d3fe743",
+ "outputId": "04eddbf2-9478-49a0-d0ad-7b1695250dda"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14:52:47 | INFO | numexpr.utils | NumExpr defaulting to 2 threads.\n",
+ "14:52:48 | INFO | datasets | TensorFlow version 2.19.0 available.\n",
+ "14:52:48 | INFO | datasets | JAX version 0.7.2 available.\n",
+ "14:53:02 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "14:53:02 | INFO | server.services.curriculum | Loaded 25 beginner tasks total\n",
+ "14:53:02 | INFO | server.services.curriculum | Loaded 25 intermediate tasks total\n",
+ "14:53:02 | INFO | server.services.curriculum | Loaded 25 advanced tasks total\n",
+ "14:53:03 | INFO | server.services.curriculum | Loaded 9 supplementary expert tasks from drift.yaml\n",
+ "14:53:03 | INFO | server.services.curriculum | Loaded 33 expert tasks total\n",
+ "14:53:03 | INFO | grpo | Loading dataset from Sizzing/aws-rl-sft …\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "README.md: 0.00B [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "17e4a25f8b0a4293b192beda65e888ab"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "data/train-00000-of-00001.parquet: 0%| | 0.00/1.92M [00:00 dict[int, Task]:\n",
+ " \"\"\"Flatten every task YAML into a dict keyed by task_id.\n",
+ "\n",
+ " The reward function only has task_id to work with; this map lets it\n",
+ " recover the full Task object needed to serialise over HTTP to /reset.\n",
+ " \"\"\"\n",
+ " m: dict[int, Task] = {}\n",
+ " for tier in TaskDifficulty:\n",
+ " try:\n",
+ " for t in load_tier(tier, tasks_dir):\n",
+ " m[int(t.task_id)] = t\n",
+ " except FileNotFoundError:\n",
+ " continue\n",
+ " return m\n",
+ "\n",
+ "\n",
+ "def load_drift_task_ids(tasks_dir: Path) -> set[int]:\n",
+ " \"\"\"Drift tasks live in drift.yaml and get folded into the EXPERT tier by\n",
+ " the curriculum loader. We scan the file directly so we can still identify\n",
+ " them for drift_repair_rate in multi-step eval.\n",
+ " \"\"\"\n",
+ " ids: set[int] = set()\n",
+ " drift_path = tasks_dir / \"drift.yaml\"\n",
+ " if drift_path.exists():\n",
+ " with open(drift_path) as f:\n",
+ " for entry in (yaml.safe_load(f) or []):\n",
+ " ids.add(int(entry[\"task_id\"]))\n",
+ " return ids\n",
+ "\n",
+ "\n",
+ "SYSTEM_PROMPT = (\n",
+ " \"You are an expert AWS Operations agent. You operate a simulated AWS cloud by \"\n",
+ " \"emitting one AWS CLI command per turn. The conversation may include prior \"\n",
+ " \"commands and their outputs from earlier in this episode — use them to decide \"\n",
+ " \"your next move.\\n\\n\"\n",
+ " \"First reason about your next move inside a ... block: identify \"\n",
+ " \"the AWS service, the right subcommand, required arguments, and any constraints \"\n",
+ " \"from the task. Keep the reasoning concise (a few short sentences).\\n\\n\"\n",
+ " \"After , on a NEW LINE, output EXACTLY ONE AWS CLI command starting \"\n",
+ " \"with \\\"aws \\\". The command line must contain only the command — no markdown, \"\n",
+ " \"no backticks, no quotes around it, and no trailing commentary.\"\n",
+ ")\n",
+ "\n",
+ "\n",
+ "def task_to_row(task: Task) -> dict:\n",
+ " \"\"\"Convert a Task into the (prompt, task_id, difficulty) schema the\n",
+ " curriculum-aware sampler reads. `remove_unused_columns=False` in the\n",
+ " trainer config keeps `difficulty` on the dataset; reward funcs accept\n",
+ " **kw so the extra column is harmlessly ignored there.\n",
+ " \"\"\"\n",
+ " return {\n",
+ " \"prompt\": [\n",
+ " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
+ " {\"role\": \"user\", \"content\": f\"TASK: {task.description}\"},\n",
+ " ],\n",
+ " \"task_id\": int(task.task_id),\n",
+ " \"difficulty\": task.difficulty.value,\n",
+ " }\n",
+ "\n",
+ "\n",
+ "def make_curriculum_dataset(curriculum: Curriculum, n_rows: int) -> Dataset:\n",
+ " \"\"\"Pre-materialise N curriculum-picked prompts as a finite `datasets.Dataset`.\n",
+ "\n",
+ " TRL's GRPOTrainer explicitly rejects IterableDataset (see TRL issue #3213:\n",
+ " ``NotImplementedError: Iterable datasets are not yet supported``). That\n",
+ " kills the original on-demand `curriculum.next_task()` streaming design —\n",
+ " the trainer must see a Dataset with a known `__len__`.\n",
+ "\n",
+ " Compromise: sample `n_rows` prompts up front. The curriculum's novelty +\n",
+ " weakness + recency heuristics still apply *across the draw* (every\n",
+ " `next_task()` pops the current top-of-queue and ages neighbouring tasks\n",
+ " via recency), so we get a sensibly ordered warm-start sample — not\n",
+ " uniform-random. What we lose is live re-ranking between steps: mastery\n",
+ " updates made by the reward function during training don't feed back\n",
+ " into selection until a fresh dataset is built. `curriculum.record_result`\n",
+ " still runs inside the reward function so mastery metrics remain accurate\n",
+ " for end-of-run stats.\n",
+ "\n",
+ " Size `n_rows` to `max_steps * per_device_train_batch_size *\n",
+ " gradient_accumulation_steps`, plus a buffer so `num_train_epochs=1`\n",
+ " never exhausts the dataset before `max_steps` terminates training.\n",
+ " \"\"\"\n",
+ " return Dataset.from_list(\n",
+ " [task_to_row(curriculum.next_task()) for _ in range(n_rows)]\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def make_full_curriculum_dataset(tasks_dir: Path) -> Dataset:\n",
+ " \"\"\"Build a *superset* dataset spanning every difficulty tier.\n",
+ "\n",
+ " This replaces `make_curriculum_dataset` for curriculum-driven training:\n",
+ " instead of drawing N prompts up front (which freezes the training data\n",
+ " to whatever tier the curriculum happened to start in), we materialise\n",
+ " EVERY task with its `difficulty` tag and let `CurriculumTierSampler`\n",
+ " dynamically pick indices from the curriculum's current tier at\n",
+ " iteration time. Tier promotion inside the reward callback then\n",
+ " immediately affects which prompts the next batch pulls.\n",
+ " \"\"\"\n",
+ " rows: list[dict] = []\n",
+ " for tier in TaskDifficulty:\n",
+ " for task in load_tier(tier, tasks_dir):\n",
+ " rows.append(task_to_row(task))\n",
+ " if not rows:\n",
+ " raise RuntimeError(f\"No tasks found under {tasks_dir}\")\n",
+ " ds = Dataset.from_list(rows)\n",
+ " log.info(\n",
+ " \"Full curriculum dataset: %d rows across tiers %s\",\n",
+ " len(ds),\n",
+ " sorted({r[\"difficulty\"] for r in rows}),\n",
+ " )\n",
+ " return ds\n",
+ "\n",
+ "\n",
+ "def make_optuna_task_dataset(task_map: dict[int, Task],\n",
+ " tier_counts: tuple,\n",
+ " seed: int = 42) -> Dataset:\n",
+ " \"\"\"Deterministic small task pool for Optuna trials.\n",
+ "\n",
+ " Picks N tasks per tier from `task_map` (sorted by task_id then shuffled\n",
+ " with a fixed seed so the pool is identical across re-runs). Returns a\n",
+ " Dataset with the same row schema as `make_full_curriculum_dataset`.\n",
+ " Used both as trial train_ds (curriculum sampler cycles within tier\n",
+ " pools) and as the single-step eval set for `trial_objective`.\n",
+ " \"\"\"\n",
+ " import random\n",
+ " rng = random.Random(seed)\n",
+ " rows: list[dict] = []\n",
+ " for tier_name, n in tier_counts:\n",
+ " pool = sorted(\n",
+ " (t for t in task_map.values() if t.difficulty.value == tier_name),\n",
+ " key=lambda t: int(t.task_id),\n",
+ " )\n",
+ " rng.shuffle(pool)\n",
+ " for t in pool[:n]:\n",
+ " rows.append(task_to_row(t))\n",
+ " if not rows:\n",
+ " raise RuntimeError(\"Optuna pool came out empty — check tier_counts\")\n",
+ " ds = Dataset.from_list(rows)\n",
+ " log.info(\"Optuna task pool: %d rows by tier=%s\",\n",
+ " len(ds), {k: n for k, n in tier_counts})\n",
+ " return ds\n",
+ "\n",
+ "\n",
+ "def _load_raw_dataset(dataset_repo: str) -> dict:\n",
+ " \"\"\"Load the HF dataset once; cached for reuse by val + reserve builders.\"\"\"\n",
+ " log.info(\"Loading dataset from %s …\", dataset_repo)\n",
+ " return load_dataset(dataset_repo, token=HF_TOKEN)\n",
+ "\n",
+ "\n",
+ "_RAW_DS_CACHE: dict = {}\n",
+ "\n",
+ "\n",
+ "def _get_raw(dataset_repo: str):\n",
+ " if dataset_repo not in _RAW_DS_CACHE:\n",
+ " _RAW_DS_CACHE[dataset_repo] = _load_raw_dataset(dataset_repo)\n",
+ " return _RAW_DS_CACHE[dataset_repo]\n",
+ "\n",
+ "\n",
+ "def build_val_dataset(dataset_repo: str, task_map: dict[int, Task],\n",
+ " val_size: int, seed: int = 42) -> Dataset:\n",
+ " \"\"\"Fixed validation subset for comparable before/after eval.\"\"\"\n",
+ " raw = _get_raw(dataset_repo)\n",
+ " val_single = raw[\"validation\"].filter(\n",
+ " lambda r: r[\"step_idx\"] == 0 and int(r[\"task_id\"]) in task_map\n",
+ " )\n",
+ " val = val_single.shuffle(seed=seed).select(\n",
+ " range(min(val_size, len(val_single)))\n",
+ " )\n",
+ " return val.map(\n",
+ " lambda r: {\"prompt\": r[\"messages\"][:2], \"task_id\": int(r[\"task_id\"])},\n",
+ " remove_columns=[c for c in val.column_names if c not in (\"prompt\", \"task_id\")],\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def build_reserve_dataset(dataset_repo: str,\n",
+ " task_map: dict[int, Task]) -> Dataset | None:\n",
+ " \"\"\"Reserve split for the multi-step eval.\"\"\"\n",
+ " raw = _get_raw(dataset_repo)\n",
+ " if \"reserve\" not in raw:\n",
+ " return None\n",
+ " reserve_single = raw[\"reserve\"].filter(\n",
+ " lambda r: r[\"step_idx\"] == 0 and int(r[\"task_id\"]) in task_map\n",
+ " )\n",
+ " return reserve_single.map(\n",
+ " lambda r: {\"prompt\": r[\"messages\"][:2], \"task_id\": int(r[\"task_id\"])},\n",
+ " remove_columns=[c for c in reserve_single.column_names\n",
+ " if c not in (\"prompt\", \"task_id\")],\n",
+ " )\n",
+ "\n",
+ "\n",
+ "_tasks_dir = REPO_DIR / \"server\" / \"services\" / \"tasks\"\n",
+ "TASK_MAP = build_task_map(_tasks_dir)\n",
+ "DRIFT_TASK_IDS = load_drift_task_ids(_tasks_dir)\n",
+ "VAL_DS = build_val_dataset(MODEL.dataset_repo, TASK_MAP,\n",
+ " PIPE.val_subset_size, TRAIN.seed)\n",
+ "RESERVE_DS = build_reserve_dataset(MODEL.dataset_repo, TASK_MAP)\n",
+ "OPTUNA_DS = make_optuna_task_dataset(TASK_MAP, PIPE.optuna_tier_counts, TRAIN.seed)\n",
+ "\n",
+ "log.info(\"TASK_MAP: %d tasks across %d tiers\", len(TASK_MAP), len({t.difficulty for t in TASK_MAP.values()}))\n",
+ "log.info(\"DRIFT_TASK_IDS: %d drift tasks\", len(DRIFT_TASK_IDS))\n",
+ "log.info(\"VAL_DS: %d rows (fixed, for before/after comparison)\", len(VAL_DS))\n",
+ "log.info(\"RESERVE_DS: %d rows (multi-step eval)\", len(RESERVE_DS) if RESERVE_DS else 0)\n",
+ "log.info(\"OPTUNA_DS: %d rows (used for trial training + trial val eval)\", len(OPTUNA_DS))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ebc9f27e",
+ "metadata": {
+ "id": "ebc9f27e"
+ },
+ "source": [
+ "## 8 · Reward functions + curriculum feedback\n",
+ "\n",
+ "Three reward functions passed to `GRPOTrainer.reward_funcs`:\n",
+ "\n",
+ "| Reward | Weight | Signal |\n",
+ "|------------------|-------:|------------------------------------------------------------|\n",
+ "| `env_reward` | 1.0 | Real env reward from `/reset` + `/step` against the task |\n",
+ "| `format_reward` | 0.15 | 1.0 if completion starts with `aws `, else 0.0 |\n",
+ "| `length_reward` | 0.05 | Soft length prior: 1.0 ≤120 chars, decays to 0.0 by 400 |\n",
+ "\n",
+ "`env_reward` also closes the curriculum loop. TRL emits the batch as `batch_size × num_generations` flattened completions with `task_id` repeated G times per prompt. We group by task_id, and for each group call `curriculum.record_result(task, achieved=any(r≥1.0), reward=mean)`. That one line is the bridge: TRL owns iteration, the curriculum owns task selection, and the reward function owns the feedback edge. No custom training loop; all the quality-of-life of TRL (checkpoint, resume, Optuna) is preserved.\n",
+ "\n",
+ "Thread safety: env HTTP calls run on the pool threads, but reward aggregation + `record_result` both happen on the main thread after the pool join, so no locking is needed.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "Locjvlb14umT",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Locjvlb14umT",
+ "outputId": "e9ed9e06-45f1-493b-a2be-ef1bc4302c6e"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14:53:16 | INFO | grpo | Reward smoke test on task 0: [1.0, 1.0]\n",
+ "14:53:16 | INFO | grpo | Env counters: {'success': 2, 'timeout': 0, 'conn_err': 0, 'reconnect': 0, 'last_error': None}\n"
+ ]
+ }
+ ],
+ "source": [
+ "import asyncio\n",
+ "import re\n",
+ "import threading\n",
+ "import traceback\n",
+ "from collections import defaultdict\n",
+ "from typing import Callable, Optional\n",
+ "\n",
+ "from client import AwsRlEnv\n",
+ "from models import AwsRlAction\n",
+ "from websockets.exceptions import ConnectionClosed\n",
+ "\n",
+ "\n",
+ "_THINK_BLOCK = re.compile(r\"]*>.*?\", re.DOTALL | re.IGNORECASE)\n",
+ "_OPEN_THINK = re.compile(r\"]*>.*\", re.DOTALL | re.IGNORECASE)\n",
+ "\n",
+ "\n",
+ "def extract_aws_command(raw: str) -> str:\n",
+ " # Drop any balanced ... spans first.\n",
+ " cleaned = _THINK_BLOCK.sub(\"\", raw)\n",
+ " # If a is still open (no matching ), everything after\n",
+ " # it is reasoning-in-progress, not a command — cut it.\n",
+ " cleaned = _OPEN_THINK.sub(\"\", cleaned)\n",
+ " for line in cleaned.splitlines():\n",
+ " line = line.strip().strip(\"`\").strip()\n",
+ " if line.startswith(\"aws \"):\n",
+ " return line\n",
+ " return \"aws help\"\n",
+ "\n",
+ "\n",
+ "class EnvRewardClient:\n",
+ " \"\"\"Persistent-WebSocket reward client against a pooled env server.\n",
+ "\n",
+ " Why WebSocket and not HTTP /reset + /step: under `AWS_RL_ENV_POOL_SIZE>1`\n",
+ " the server's HTTP path uses an env *factory* — every request builds a\n",
+ " fresh `AwsRlEnvironment` from the pool factory, so `/step` on request 2\n",
+ " lands on a different env than `/reset` on request 1 and trips\n",
+ " ``assert self._episode is not None, \"Call reset() before step()\"``.\n",
+ " Only WebSocket sessions hold a MiniStack slot across calls.\n",
+ "\n",
+ " Design:\n",
+ " - Dedicated background thread owns an asyncio loop.\n",
+ " - On startup, N AwsRlEnv WebSocket sessions connect in parallel and\n",
+ " sit in an asyncio.Queue acting as a free-list.\n",
+ " - score_batch() is synchronous (TRL calls reward funcs sync): it\n",
+ " submits one async task per (task, command) pair to the loop via\n",
+ " run_coroutine_threadsafe, each task acquires a free session from\n",
+ " the queue, does reset+step, returns the env to the queue.\n",
+ " - Reconnect-on-failure: Cloudflare tunnels can idle-close WS\n",
+ " sessions, but the client keeps a reference to the (now-closed)\n",
+ " socket so OpenEnv's _ensure_connected() is a no-op. We catch\n",
+ " ConnectionClosed explicitly, call disconnect(), reconnect, and\n",
+ " retry once before giving up.\n",
+ "\n",
+ " Counters (success / timeout / conn_err / reconnect) let the trainer\n",
+ " log env health without inspecting internal state. `last_error`\n",
+ " captures the most recent exception. `verbose_errors=True` prints\n",
+ " full tracebacks per failure (noisy — only enable while debugging).\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(\n",
+ " self,\n",
+ " base_url: str,\n",
+ " pool_size: int = 8,\n",
+ " timeout_s: float = 60.0,\n",
+ " verbose_errors: bool = False,\n",
+ " ):\n",
+ " self.base_url = base_url\n",
+ " self.pool_size = pool_size\n",
+ " self.timeout_s = timeout_s\n",
+ " self.verbose_errors = verbose_errors\n",
+ " self.success = 0\n",
+ " self.timeout = 0\n",
+ " self.conn_err = 0\n",
+ " self.reconnect = 0\n",
+ " self.last_error: Optional[str] = None\n",
+ " self._loop: Optional[asyncio.AbstractEventLoop] = None\n",
+ " self._thread: Optional[threading.Thread] = None\n",
+ " self._queue: Optional[asyncio.Queue] = None\n",
+ " self._envs: list = []\n",
+ " self._ready = threading.Event()\n",
+ " self._setup_error: Optional[BaseException] = None\n",
+ " self._start()\n",
+ "\n",
+ " def _start(self) -> None:\n",
+ " def run():\n",
+ " loop = asyncio.new_event_loop()\n",
+ " self._loop = loop\n",
+ " asyncio.set_event_loop(loop)\n",
+ " try:\n",
+ " loop.run_until_complete(self._setup())\n",
+ " except BaseException as e:\n",
+ " self._setup_error = e\n",
+ " self._ready.set()\n",
+ " return\n",
+ " self._ready.set()\n",
+ " loop.run_forever()\n",
+ "\n",
+ " self._thread = threading.Thread(target=run, daemon=True, name=\"env-reward\")\n",
+ " self._thread.start()\n",
+ " self._ready.wait()\n",
+ " if self._setup_error is not None:\n",
+ " raise RuntimeError(\n",
+ " f\"EnvRewardClient failed to connect {self.pool_size} WS sessions \"\n",
+ " f\"to {self.base_url}: {self._setup_error!r}\"\n",
+ " )\n",
+ "\n",
+ " async def _setup(self) -> None:\n",
+ " self._queue = asyncio.Queue(self.pool_size)\n",
+ " self._envs = [AwsRlEnv(base_url=self.base_url) for _ in range(self.pool_size)]\n",
+ " try:\n",
+ " await asyncio.gather(*(e.connect() for e in self._envs))\n",
+ " except BaseException:\n",
+ " await asyncio.gather(\n",
+ " *(e.close() for e in self._envs), return_exceptions=True\n",
+ " )\n",
+ " raise\n",
+ " for e in self._envs:\n",
+ " self._queue.put_nowait(e)\n",
+ "\n",
+ " async def _reconnect(self, env) -> None:\n",
+ " \"\"\"Discard the dead socket and open a fresh WS session in-place.\"\"\"\n",
+ " self.reconnect += 1\n",
+ " try:\n",
+ " await env.disconnect()\n",
+ " except Exception:\n",
+ " pass\n",
+ " await env.connect()\n",
+ "\n",
+ " async def _reset_and_step(self, env, task: Task, command: str) -> float:\n",
+ " await asyncio.wait_for(env.reset(task=task), timeout=self.timeout_s)\n",
+ " res = await asyncio.wait_for(\n",
+ " env.step(AwsRlAction(command=command)), timeout=self.timeout_s\n",
+ " )\n",
+ " return float(res.reward)\n",
+ "\n",
+ " async def _score_one(self, task: Task, command: str) -> float:\n",
+ " env = await self._queue.get()\n",
+ " try:\n",
+ " try:\n",
+ " reward = await self._reset_and_step(env, task, command)\n",
+ " self.success += 1\n",
+ " return reward\n",
+ " except ConnectionClosed as e:\n",
+ " # Cloudflare / server idle-closed the socket. Reconnect and retry.\n",
+ " self.last_error = f\"reconnect after {type(e).__name__}: {e}\"\n",
+ " if self.verbose_errors:\n",
+ " print(f\"[reward] {self.last_error} — reconnecting\")\n",
+ " await self._reconnect(env)\n",
+ " reward = await self._reset_and_step(env, task, command)\n",
+ " self.success += 1\n",
+ " return reward\n",
+ " except asyncio.TimeoutError:\n",
+ " self.timeout += 1\n",
+ " self.last_error = \"asyncio.TimeoutError\"\n",
+ " if self.verbose_errors:\n",
+ " traceback.print_exc()\n",
+ " return 0.0\n",
+ " except Exception as e:\n",
+ " self.conn_err += 1\n",
+ " self.last_error = f\"{type(e).__name__}: {e}\"\n",
+ " if self.verbose_errors:\n",
+ " traceback.print_exc()\n",
+ " return 0.0\n",
+ " finally:\n",
+ " self._queue.put_nowait(env)\n",
+ "\n",
+ " async def _score_batch_async(\n",
+ " self, tasks: list, commands: list[str]\n",
+ " ) -> list[float]:\n",
+ " return list(\n",
+ " await asyncio.gather(\n",
+ " *(self._score_one(t, c) for t, c in zip(tasks, commands))\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " def score_batch(self, tasks: list, commands: list[str]) -> list[float]:\n",
+ " \"\"\"Parallel scoring; preserves input order.\"\"\"\n",
+ " assert self._loop is not None\n",
+ " fut = asyncio.run_coroutine_threadsafe(\n",
+ " self._score_batch_async(tasks, commands), self._loop\n",
+ " )\n",
+ " return fut.result()\n",
+ "\n",
+ " async def _rollout_one_async(\n",
+ " self,\n",
+ " task: Task,\n",
+ " first_command: str,\n",
+ " generate_next, # sync callable (history) -> str\n",
+ " max_steps: int,\n",
+ " ) -> float:\n",
+ " \"\"\"Interactive multi-turn rollout on a single env session.\n",
+ "\n",
+ " Plays `first_command` as turn 1 (this is what TRL just generated),\n",
+ " then for turns 2..max_steps asks `generate_next` for the next\n",
+ " command given the running (cmd, output) history. Used by\n",
+ " `score_batch_interactive` to give the trainer the same multi-step\n",
+ " rollout semantics as eval-time `run_episode`.\n",
+ "\n",
+ " `generate_next` is sync because the policy lives on the main\n",
+ " thread's GPU; we call it from this background-thread coroutine\n",
+ " via `loop.run_in_executor(None, ...)` so it doesn't block the\n",
+ " asyncio loop.\n",
+ " \"\"\"\n",
+ " env = await self._queue.get()\n",
+ " try:\n",
+ " try:\n",
+ " await asyncio.wait_for(env.reset(task=task), timeout=self.timeout_s)\n",
+ " history: list[tuple[str, str]] = []\n",
+ " reward = 0.0\n",
+ " cmd = first_command\n",
+ " loop = asyncio.get_running_loop()\n",
+ " for turn in range(max_steps):\n",
+ " res = await asyncio.wait_for(\n",
+ " env.step(AwsRlAction(command=cmd)),\n",
+ " timeout=self.timeout_s,\n",
+ " )\n",
+ " reward = float(res.reward)\n",
+ " obs = getattr(res, \"observation\", None)\n",
+ " history.append((cmd, getattr(obs, \"command_output\", \"\") or \"\"))\n",
+ " done_flag = getattr(res, \"done\", False) or getattr(\n",
+ " obs, \"task_achieved\", False\n",
+ " )\n",
+ " if done_flag or turn == max_steps - 1:\n",
+ " break\n",
+ " # Hop to a worker thread for the (sync, GPU-bound) generation.\n",
+ " cmd = await loop.run_in_executor(\n",
+ " None, generate_next, tuple(history)\n",
+ " )\n",
+ " self.success += 1\n",
+ " return reward\n",
+ " except ConnectionClosed as e:\n",
+ " # Don't auto-retry mid-episode: a fresh reset would wipe\n",
+ " # tracker state and any partial credit from earlier turns.\n",
+ " self.last_error = f\"{type(e).__name__}: {e} (mid-rollout)\"\n",
+ " if self.verbose_errors:\n",
+ " print(f\"[reward] {self.last_error}\")\n",
+ " self.conn_err += 1\n",
+ " return 0.0\n",
+ " except asyncio.TimeoutError:\n",
+ " self.timeout += 1\n",
+ " self.last_error = \"asyncio.TimeoutError\"\n",
+ " if self.verbose_errors:\n",
+ " traceback.print_exc()\n",
+ " return 0.0\n",
+ " except Exception as e:\n",
+ " self.conn_err += 1\n",
+ " self.last_error = f\"{type(e).__name__}: {e}\"\n",
+ " if self.verbose_errors:\n",
+ " traceback.print_exc()\n",
+ " return 0.0\n",
+ " finally:\n",
+ " self._queue.put_nowait(env)\n",
+ "\n",
+ " def score_batch_interactive(\n",
+ " self,\n",
+ " tasks: list,\n",
+ " first_commands: list[str],\n",
+ " generate_next_per_rollout: list, # list[Callable[[history], str]]\n",
+ " max_steps: int = 5,\n",
+ " ) -> list[float]:\n",
+ " \"\"\"Parallel multi-turn scoring; preserves input order.\n",
+ "\n",
+ " Each rollout takes ONE command at a time: TRL's completion\n",
+ " supplies turn 1, then `generate_next_per_rollout[i](history)`\n",
+ " is called for each subsequent turn of rollout `i` until\n",
+ " `done=True` or `max_steps` is reached. Up to `pool_size`\n",
+ " rollouts run in parallel against the WS pool — but the caller\n",
+ " is expected to serialise GPU-bound generates inside the\n",
+ " closures themselves (one shared lock), since concurrent\n",
+ " generates on a single accelerator only fight for the same\n",
+ " memory.\n",
+ " \"\"\"\n",
+ " assert self._loop is not None\n",
+ " assert len(tasks) == len(first_commands) == len(generate_next_per_rollout)\n",
+ "\n",
+ " async def _gather():\n",
+ " return list(await asyncio.gather(*(\n",
+ " self._rollout_one_async(t, c, gen, max_steps)\n",
+ " for t, c, gen in zip(\n",
+ " tasks, first_commands, generate_next_per_rollout,\n",
+ " )\n",
+ " )))\n",
+ "\n",
+ " fut = asyncio.run_coroutine_threadsafe(_gather(), self._loop)\n",
+ " return fut.result()\n",
+ "\n",
+ " def drain_counters(self) -> dict:\n",
+ " out = {\n",
+ " \"success\": self.success,\n",
+ " \"timeout\": self.timeout,\n",
+ " \"conn_err\": self.conn_err,\n",
+ " \"reconnect\": self.reconnect,\n",
+ " \"last_error\": self.last_error,\n",
+ " }\n",
+ " self.success = self.timeout = self.conn_err = self.reconnect = 0\n",
+ " self.last_error = None\n",
+ " return out\n",
+ "\n",
+ " def close(self) -> None:\n",
+ " if self._loop is None or not self._loop.is_running():\n",
+ " return\n",
+ "\n",
+ " async def _close():\n",
+ " await asyncio.gather(\n",
+ " *(e.disconnect() for e in self._envs), return_exceptions=True\n",
+ " )\n",
+ "\n",
+ " try:\n",
+ " asyncio.run_coroutine_threadsafe(_close(), self._loop).result(timeout=30)\n",
+ " except Exception:\n",
+ " pass\n",
+ " self._loop.call_soon_threadsafe(self._loop.stop)\n",
+ " if self._thread is not None:\n",
+ " self._thread.join(timeout=5)\n",
+ "\n",
+ "\n",
+ "def build_reward_funcs(env: EnvRewardClient,\n",
+ " task_map: dict[int, Task],\n",
+ " curriculum: Optional[Curriculum] = None,\n",
+ " model=None,\n",
+ " tokenizer=None,\n",
+ " max_rollout_steps: int = 5,\n",
+ " ) -> tuple[Callable, Callable, Callable]:\n",
+ " \"\"\"Return (env_reward, format_reward, length_reward).\n",
+ "\n",
+ " When `curriculum` is provided, env_reward calls curriculum.record_result()\n",
+ " once per unique task_id in the batch — one record = one group = one\n",
+ " curriculum episode. This is what makes the training loop curriculum-driven\n",
+ " even while TRL owns the outer iteration.\n",
+ "\n",
+ " When `model` and `tokenizer` are provided, env_reward runs INTERACTIVE\n",
+ " multi-turn rollouts: TRL's completion supplies the first command, then\n",
+ " the policy is re-queried after each env step (with running command/output\n",
+ " history) for up to `max_rollout_steps - 1` more turns. This is what lets\n",
+ " multi-step (intermediate/advanced/expert) tasks actually achieve during\n",
+ " training. Without `model`, env_reward falls back to single-step scoring.\n",
+ " \"\"\"\n",
+ " def _text_of(c) -> str:\n",
+ " return c if isinstance(c, str) else c[0][\"content\"]\n",
+ "\n",
+ " # Pre-build the multi-turn generate_next closure once. Only used when\n",
+ " # `model` was supplied — single-step path skips it entirely.\n",
+ " if model is not None and tokenizer is not None:\n",
+ " import torch as _torch # local import to avoid hard dep if unused\n",
+ " _gen_lock = threading.Lock() # GPU serialisation across rollouts\n",
+ "\n",
+ " def _generate_next_for_task(task: Task, history) -> str:\n",
+ " \"\"\"Sync per-turn generation: build prompt with running history,\n",
+ " decode one command, return the extracted `aws ...` line.\n",
+ " Runs on a worker thread off the event loop (see\n",
+ " `_rollout_one_async`); the lock prevents concurrent rollouts\n",
+ " from racing on the same GPU.\"\"\"\n",
+ " messages = [\n",
+ " {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
+ " {\"role\": \"user\", \"content\": f\"TASK: {task.description}\"},\n",
+ " ]\n",
+ " for cmd, out in list(history)[-4:]:\n",
+ " messages.append({\"role\": \"assistant\", \"content\": cmd})\n",
+ " messages.append({\"role\": \"user\", \"content\": f\"OUTPUT:\\n{out[:400]}\"})\n",
+ " prompt_text = tokenizer.apply_chat_template(\n",
+ " messages, tokenize=False, add_generation_prompt=True,\n",
+ " )\n",
+ " with _gen_lock:\n",
+ " inputs = tokenizer(prompt_text, return_tensors=\"pt\").to(model.device)\n",
+ " with _torch.inference_mode():\n",
+ " ids = model.generate(\n",
+ " **inputs,\n",
+ " max_new_tokens=256,\n",
+ " do_sample=True, temperature=0.7, top_p=0.9,\n",
+ " pad_token_id=tokenizer.eos_token_id,\n",
+ " )\n",
+ " text = tokenizer.decode(\n",
+ " ids[0, inputs.input_ids.shape[1]:], skip_special_tokens=True,\n",
+ " )\n",
+ " return extract_aws_command(text)\n",
+ " else:\n",
+ " _generate_next_for_task = None\n",
+ "\n",
+ " def env_reward(prompts, completions, task_id=None, **kw):\n",
+ " tids = task_id if task_id is not None else kw[\"task_id\"]\n",
+ " tasks = [task_map[int(t)] for t in tids]\n",
+ " cmds = [extract_aws_command(_text_of(c)) for c in completions]\n",
+ "\n",
+ " if _generate_next_for_task is not None:\n",
+ " # Interactive multi-turn: one task-bound generate_next closure\n",
+ " # per rollout; env.step calls fan out across the WS pool, while\n",
+ " # the shared `_gen_lock` inside the closure keeps generates\n",
+ " # serial on the single GPU.\n",
+ " gens = [\n",
+ " (lambda hist, _t=t: _generate_next_for_task(_t, hist))\n",
+ " for t in tasks\n",
+ " ]\n",
+ " rewards = env.score_batch_interactive(\n",
+ " tasks, cmds, gens, max_steps=max_rollout_steps,\n",
+ " )\n",
+ " else:\n",
+ " rewards = env.score_batch(tasks, cmds)\n",
+ "\n",
+ " # Group by task_id and feed each group back to the curriculum. TRL emits\n",
+ " # G completions per prompt consecutively (all sharing one task_id), so\n",
+ " # grouping recovers the GRPO semantics cleanly: one record per prompt,\n",
+ " # achieved iff any rollout hit reward>=1.0, recorded reward = group mean.\n",
+ " if curriculum is not None:\n",
+ " by_tid: dict[int, list[float]] = defaultdict(list)\n",
+ " for tid, r in zip(tids, rewards):\n",
+ " by_tid[int(tid)].append(r)\n",
+ " for tid, group in by_tid.items():\n",
+ " curriculum.record_result(\n",
+ " task_map[tid],\n",
+ " achieved=any(r >= 1.0 for r in group),\n",
+ " reward=sum(group) / len(group),\n",
+ " )\n",
+ " return rewards\n",
+ "\n",
+ " def format_reward(prompts, completions, **kw):\n",
+ " # Thinking is now allowed before the command, so the old\n",
+ " # \"first char must be aws \" check would score 0 on every\n",
+ " # well-formed ... + aws ... output. Instead\n",
+ " # require that *some* line of the completion starts with\n",
+ " # \"aws \" — the same contract extract_aws_command() relies on.\n",
+ " out = []\n",
+ " for c in completions:\n",
+ " txt = _text_of(c)\n",
+ " has_cmd = any(\n",
+ " line.strip().strip(\"`\").strip().startswith(\"aws \")\n",
+ " for line in txt.splitlines()\n",
+ " )\n",
+ " out.append(1.0 if has_cmd else 0.0)\n",
+ " return out\n",
+ "\n",
+ " def length_reward(prompts, completions, **kw):\n",
+ " # With ... the completion can reasonably run to a\n",
+ " # couple of thousand characters; the old 120-char cap would pin\n",
+ " # length_reward to ~0 on every thinking output. Grade the extracted\n",
+ " # command line for concision (commands themselves are still short)\n",
+ " # and only mildly penalise extreme verbosity in the surrounding\n",
+ " # reasoning so the model is nudged away from rambling.\n",
+ " out = []\n",
+ " for c in completions:\n",
+ " txt = _text_of(c)\n",
+ " cmd = extract_aws_command(txt)\n",
+ " cmd_n = len(cmd)\n",
+ " cmd_score = 1.0 if cmd_n <= 120 else max(\n",
+ " 0.0, 1.0 - (cmd_n - 120) / 280.0\n",
+ " )\n",
+ " total_n = len(txt)\n",
+ " verbosity_score = 1.0 if total_n <= 2000 else max(\n",
+ " 0.0, 1.0 - (total_n - 2000) / 2000.0\n",
+ " )\n",
+ " out.append(0.5 * cmd_score + 0.5 * verbosity_score)\n",
+ " return out\n",
+ "\n",
+ " return env_reward, format_reward, length_reward\n",
+ "\n",
+ "\n",
+ "# Re-run protection: if this cell has been run before, the old ENV_CLIENT's\n",
+ "# background thread is still holding 8 WebSocket sessions. Close it cleanly\n",
+ "# (sends {\"type\":\"close\"} so the server's /ws handler reaches its finally\n",
+ "# block and calls _destroy_session → releases the MiniStack slot). Without\n",
+ "# this, every re-run compounds the leak until the server hits 8/8 capacity.\n",
+ "try:\n",
+ " _prev = ENV_CLIENT # noqa: F821\n",
+ "except NameError:\n",
+ " pass\n",
+ "else:\n",
+ " log.info(\"Closing previous ENV_CLIENT to release its WS sessions…\")\n",
+ " try:\n",
+ " _prev.close()\n",
+ " except Exception as _e:\n",
+ " log.warning(\"Ignored error during previous close: %r\", _e)\n",
+ "\n",
+ "# verbose_errors=True for the first run so the smoke test surfaces any WS /\n",
+ "# reset / step exception with a full traceback. Flip off after it passes.\n",
+ "ENV_CLIENT = EnvRewardClient(\n",
+ " base_url=ENV_BASE_URL,\n",
+ " pool_size=PIPE.env_pool_size,\n",
+ " verbose_errors=True,\n",
+ ")\n",
+ "\n",
+ "# Smoke test uses a non-curriculum client to avoid polluting any curriculum state.\n",
+ "# Task 0 (\"List all S3 buckets\") matches the `aws s3 ls` completion.\n",
+ "_smoke_task = TASK_MAP[0]\n",
+ "_smoke_env, _smoke_fmt, _smoke_len = build_reward_funcs(ENV_CLIENT, TASK_MAP, curriculum=None)\n",
+ "_smoke = _smoke_env(\n",
+ " prompts=[None] * 2,\n",
+ " completions=[\"aws s3 ls\", \"aws s3 ls\"],\n",
+ " task_id=[_smoke_task.task_id, _smoke_task.task_id],\n",
+ ")\n",
+ "log.info(\"Reward smoke test on task %s: %s\", _smoke_task.task_id, _smoke)\n",
+ "log.info(\"Env counters: %s\", ENV_CLIENT.drain_counters())\n",
+ "assert min(_smoke) > 0.5, \"Reward smoke test failed — env or reward wiring broken\"\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99f34cfe",
+ "metadata": {
+ "id": "99f34cfe"
+ },
+ "source": [
+ "## 9 · Load the SFT adapter as the starting policy\n",
+ "\n",
+ "We go through `PeftModel.from_pretrained(base, sft_adapter, is_trainable=True)` explicitly (rather than `FastLanguageModel.from_pretrained(adapter_repo)`) so there is no ambiguity about the adapter landing in trainable mode — GRPO must be able to update the LoRA weights.\n",
+ "\n",
+ "This helper is also used later by the final-run and Optuna-trial paths, so it lives in its own cell."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "e360207d",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 531,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "e360207d",
+ "outputId": "e8587f47-92cf-4090-86c7-caa75d5cdabf"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
+ "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
+ "==((====))== Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 4.57.6.\n",
+ " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n",
+ "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.35. FA2 = False]\n",
+ " \"-____-\" Free license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "model.safetensors: 0%| | 0.00/2.05G [00:00 None:\n",
+ " \"\"\"Release GPU memory held by a model + its optimizer state.\"\"\"\n",
+ " del model\n",
+ " gc.collect()\n",
+ " torch.cuda.empty_cache()\n",
+ "\n",
+ "\n",
+ "# Sanity: load + confirm LoRA params are trainable.\n",
+ "_probe_model, _probe_tok = load_policy(MODEL, trainable=True)\n",
+ "_trainable = [n for n, p in _probe_model.named_parameters() if p.requires_grad]\n",
+ "log.info(\"Loaded %s. Trainable params: %d tensors; sample: %s\", MODEL.sft_adapter, len(_trainable), _trainable[:3])\n",
+ "assert any(\"lora\" in n.lower() for n in _trainable), \"No LoRA params marked trainable — load path is wrong\"\n",
+ "free_model(_probe_model)\n",
+ "del _probe_tok\n",
+ "gc.collect(); torch.cuda.empty_cache()\n",
+ "log.info(\"Model load path verified.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "13c4aa8f",
+ "metadata": {
+ "id": "13c4aa8f"
+ },
+ "source": [
+ "## 10 · Baseline eval — SFT adapter, single-step env reward\n",
+ "\n",
+ "Single-pass eval on the val set. This is the \"before\" column of the headline comparison. The richer *multi-step* eval happens later; this one is only here to confirm the SFT-loaded model outputs sane commands against the env **before** we start GRPO.\n",
+ "\n",
+ "Written as a small `evaluate_single_step` helper because GRPO's inner trial loop needs the same logic."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "eval-single-step",
+ "metadata": {
+ "id": "eval-single-step",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "41ff1041-4807-41a3-d2cc-bdc6c89a985b"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "==((====))== Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 4.57.6.\n",
+ " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n",
+ "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.35. FA2 = False]\n",
+ " \"-____-\" Free license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
+ "14:56:31 | INFO | grpo | SFT baseline (single-step, val): {'env_reward_mean': 0.9, 'env_success_rate': 0.85, 'format_pct': 1.0, 'n': 20}\n"
+ ]
+ }
+ ],
+ "source": [
+ "import json\n",
+ "import statistics as _stats\n",
+ "from dataclasses import asdict\n",
+ "\n",
+ "\n",
+ "@dataclass\n",
+ "class SingleStepMetrics:\n",
+ " \"\"\"One row of headline comparison numbers.\"\"\"\n",
+ " env_reward_mean: float\n",
+ " env_success_rate: float # fraction with reward >= 1.0\n",
+ " format_pct: float\n",
+ " n: int\n",
+ "\n",
+ " def as_dict(self) -> dict:\n",
+ " return asdict(self)\n",
+ "\n",
+ "\n",
+ "def evaluate_single_step(model, tokenizer, dataset, env: EnvRewardClient,\n",
+ " task_map: dict[int, Task],\n",
+ " max_new_tokens: int = 128) -> SingleStepMetrics:\n",
+ " \"\"\"Generate one command per prompt, score against the env, summarize.\"\"\"\n",
+ " from unsloth import FastLanguageModel\n",
+ "\n",
+ " FastLanguageModel.for_inference(model)\n",
+ " formats: list[float] = []\n",
+ " tasks_to_score: list[Task] = []\n",
+ " cmds_to_score: list[str] = []\n",
+ "\n",
+ " for row in dataset:\n",
+ " prompt_text = tokenizer.apply_chat_template(\n",
+ " row[\"prompt\"], tokenize=False, add_generation_prompt=True,\n",
+ " )\n",
+ " inputs = tokenizer(prompt_text, return_tensors=\"pt\").to(model.device)\n",
+ " with torch.inference_mode():\n",
+ " ids = model.generate(\n",
+ " **inputs,\n",
+ " max_new_tokens=max_new_tokens,\n",
+ " do_sample=False,\n",
+ " pad_token_id=tokenizer.eos_token_id,\n",
+ " )\n",
+ " text = tokenizer.decode(\n",
+ " ids[0, inputs.input_ids.shape[1]:], skip_special_tokens=True,\n",
+ " )\n",
+ " # Match the training-time format_reward contract: accept any\n",
+ " # line starting with \"aws \" so a ... prefix does\n",
+ " # not score 0 for format.\n",
+ " has_cmd = any(\n",
+ " line.strip().strip(\"`\").strip().startswith(\"aws \")\n",
+ " for line in text.splitlines()\n",
+ " )\n",
+ " formats.append(1.0 if has_cmd else 0.0)\n",
+ " tasks_to_score.append(task_map[int(row[\"task_id\"])])\n",
+ " cmds_to_score.append(extract_aws_command(text))\n",
+ "\n",
+ " # Score all env rewards in parallel across the 8 server slots\n",
+ " rewards = env.score_batch(tasks_to_score, cmds_to_score)\n",
+ "\n",
+ " FastLanguageModel.for_training(model)\n",
+ "\n",
+ " return SingleStepMetrics(\n",
+ " env_reward_mean=float(_stats.mean(rewards)),\n",
+ " env_success_rate=sum(r >= 1.0 for r in rewards) / len(rewards),\n",
+ " format_pct=float(_stats.mean(formats)),\n",
+ " n=len(rewards),\n",
+ " )\n",
+ "\n",
+ "\n",
+ "# Run the SFT-only baseline and persist it alongside Optuna + checkpoints\n",
+ "_baseline_model, _baseline_tok = load_policy(MODEL, trainable=False)\n",
+ "baseline_metrics = evaluate_single_step(\n",
+ " _baseline_model, _baseline_tok, VAL_DS, ENV_CLIENT, TASK_MAP,\n",
+ " max_new_tokens=TRAIN.max_completion_length,\n",
+ ")\n",
+ "(OUT_DIR / \"baseline_single_step.json\").write_text(json.dumps(baseline_metrics.as_dict(), indent=2))\n",
+ "free_model(_baseline_model); del _baseline_tok\n",
+ "gc.collect(); torch.cuda.empty_cache()\n",
+ "\n",
+ "log.info(\"SFT baseline (single-step, val): %s\", baseline_metrics.as_dict())\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "b439f160",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "b439f160",
+ "outputId": "98e86e8f-3142-46df-8403-7835d07ce8a9"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14:56:31 | INFO | grpo | Loading optuna and defining trial objective...\n",
+ "14:56:31 | INFO | grpo | Curriculum-driven Optuna objective defined.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import optuna\n",
+ "import time\n",
+ "\n",
+ "log.info(\"Loading optuna and defining trial objective...\")\n",
+ "\n",
+ "\n",
+ "def suggest_training_config(trial: optuna.Trial, defaults: TrainingConfig) -> TrainingConfig:\n",
+ " \"\"\"Return a mutated copy of `defaults` with Optuna-sampled hparams.\n",
+ "\n",
+ " One function, one diff — keeps the search space auditable.\n",
+ " \"\"\"\n",
+ " return replace(\n",
+ " defaults,\n",
+ " learning_rate = trial.suggest_float(\"learning_rate\", 1e-6, 5e-5, log=True),\n",
+ " beta = trial.suggest_float(\"beta\", 0.0, 0.1),\n",
+ " temperature = trial.suggest_float(\"temperature\", 0.7, 1.0),\n",
+ " )\n",
+ "\n",
+ "\n",
+ "def trial_objective(trial: optuna.Trial) -> float:\n",
+ " \"\"\"Short curriculum-driven GRPO run + val eval. Returns mean env reward\n",
+ " on the small Optuna task pool.\"\"\"\n",
+ " trial_cfg = suggest_training_config(trial, TRAIN)\n",
+ " output_dir = str(OUT_DIR / f\"optuna/trial-{trial.number}\")\n",
+ " log.info(\"[trial %d] starting | cfg=%s\", trial.number, {\n",
+ " \"lr\": trial_cfg.learning_rate, \"beta\": trial_cfg.beta,\n",
+ " \"temp\": trial_cfg.temperature,\n",
+ " })\n",
+ "\n",
+ " # Fresh curriculum per trial — otherwise mastery and tier progression bleed\n",
+ " # across trials, making Optuna's hparam comparison unfair.\n",
+ " trial_curriculum = Curriculum(tasks_dir=_tasks_dir)\n",
+ " # Small fixed task pool (12 rows: warmup-2, beginner-2, intermediate-2,\n",
+ " # advanced-3, expert-3). CurriculumTierSampler still cycles within whatever\n",
+ " # tier the curriculum is on; with only 2-3 rows per tier the sampler\n",
+ " # simply re-shuffles those indices each cycle.\n",
+ " trial_train_ds = OPTUNA_DS\n",
+ " trial_num_samples = int(\n",
+ " PIPE.trial_max_steps\n",
+ " * trial_cfg.per_device_train_batch_size\n",
+ " * trial_cfg.gradient_accumulation_steps\n",
+ " * 1.2\n",
+ " )\n",
+ " log.info(\"[trial %d] loading SFT policy (4-bit Qwen2.5-Coder-3B)...\", trial.number)\n",
+ " _t0 = time.time()\n",
+ " model, tokenizer = load_policy(MODEL, trainable=True)\n",
+ " log.info(\"[trial %d] policy loaded in %.1fs\", trial.number, time.time() - _t0)\n",
+ "\n",
+ " trial_env_r, trial_fmt_r, trial_len_r = build_reward_funcs(\n",
+ " ENV_CLIENT, TASK_MAP, trial_curriculum,\n",
+ " model=model, tokenizer=tokenizer,\n",
+ " )\n",
+ "\n",
+ " trainer = build_trainer(\n",
+ " model, tokenizer,\n",
+ " train_ds=trial_train_ds,\n",
+ " eval_ds=OPTUNA_DS,\n",
+ " reward_funcs=(trial_env_r, trial_fmt_r, trial_len_r),\n",
+ " cfg=trial_cfg,\n",
+ " output_dir=output_dir,\n",
+ " run_name=f\"optuna-trial-{trial.number}\",\n",
+ " use_fp16=RT.use_fp16, use_bf16=RT.use_bf16,\n",
+ " max_steps=PIPE.trial_max_steps,\n",
+ " save_strategy=\"no\",\n",
+ " curriculum=trial_curriculum,\n",
+ " num_samples=trial_num_samples,\n",
+ " )\n",
+ "\n",
+ " try:\n",
+ " log.info(\"[trial %d] starting train() for %d steps...\",\n",
+ " trial.number, PIPE.trial_max_steps)\n",
+ " _t1 = time.time()\n",
+ " trainer.train()\n",
+ " log.info(\"[trial %d] train() finished in %.1fs\", trial.number, time.time() - _t1)\n",
+ " # Persist trainer_state.json for later plotting. save_strategy=\"no\"\n",
+ " # means TRL never writes checkpoints during training, so without\n",
+ " # this call there'd be no on-disk log history for this trial once\n",
+ " # the process exits.\n",
+ " trial_out = Path(output_dir)\n",
+ " trial_out.mkdir(parents=True, exist_ok=True)\n",
+ " (trial_out / \"trainer_state.json\").write_text(\n",
+ " json.dumps(\n",
+ " {\"log_history\": trainer.state.log_history,\n",
+ " \"global_step\": trainer.state.global_step,\n",
+ " \"trial_number\": trial.number},\n",
+ " indent=2, default=str,\n",
+ " )\n",
+ " )\n",
+ " # Score the trial on the same small Optuna task pool.\n",
+ " log.info(\"[trial %d] running single-step eval on %d tasks...\",\n",
+ " trial.number, len(OPTUNA_DS))\n",
+ " _t2 = time.time()\n",
+ " metrics = evaluate_single_step(\n",
+ " trainer.model, tokenizer, OPTUNA_DS, ENV_CLIENT, TASK_MAP,\n",
+ " max_new_tokens=trial_cfg.max_completion_length,\n",
+ " )\n",
+ " log.info(\"[trial %d] eval finished in %.1fs\", trial.number, time.time() - _t2)\n",
+ " score = metrics.env_reward_mean\n",
+ " # Also capture single-step eval metrics per trial for offline plotting.\n",
+ " (trial_out / \"single_step_metrics.json\").write_text(\n",
+ " json.dumps(metrics.as_dict(), indent=2)\n",
+ " )\n",
+ " log.info(\n",
+ " \"[trial %d] DONE | env_reward_mean=%.4f success=%.3f tier=%s graduated=%d\",\n",
+ " trial.number, score, metrics.env_success_rate,\n",
+ " trial_curriculum.current_difficulty.value,\n",
+ " len(trial_curriculum.get_stats().get(\"graduated_tasks\", [])),\n",
+ " )\n",
+ " finally:\n",
+ " free_model(trainer); free_model(model); del tokenizer\n",
+ " gc.collect(); torch.cuda.empty_cache()\n",
+ "\n",
+ " trial.report(score, step=PIPE.trial_max_steps)\n",
+ " if trial.should_prune():\n",
+ " raise optuna.TrialPruned()\n",
+ " return score\n",
+ "\n",
+ "\n",
+ "log.info(\"Curriculum-driven Optuna objective defined.\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "6f37100a",
+ "metadata": {
+ "id": "6f37100a",
+ "outputId": "09fc6f50-5d1e-467a-f410-35d549bc33f4",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14:56:32 | INFO | grpo | GRPOTrainer factory ready.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import random\n",
+ "import time\n",
+ "from collections import defaultdict\n",
+ "from collections.abc import Iterable\n",
+ "\n",
+ "import torch\n",
+ "from trl import GRPOConfig, GRPOTrainer\n",
+ "from transformers import TrainerCallback\n",
+ "\n",
+ "\n",
+ "class CurriculumTierSampler(torch.utils.data.Sampler[int]):\n",
+ " \"\"\"Reactive sampler: yields dataset indices from the curriculum's\n",
+ " *current* tier, re-checked on every yield.\n",
+ "\n",
+ " The usual approach — pre-materialising a fixed dataset from the\n",
+ " curriculum — freezes the training data to whatever tier the curriculum\n",
+ " started in, because `curriculum.next_task()` never calls\n",
+ " `record_result` and so `_maybe_promote` can't fire during the draw.\n",
+ " Here the dataset is a *superset* of all tiers (see\n",
+ " make_full_curriculum_dataset) and this sampler picks indices only\n",
+ " from rows whose `difficulty` matches the curriculum's current tier.\n",
+ " When `env_reward` promotes the curriculum mid-training, the very\n",
+ " next yield flips onto the new tier's index pool.\n",
+ "\n",
+ " Implementation notes:\n",
+ " * `__len__` is required for HF Trainer's max_steps math. We return\n",
+ " `num_samples`, which the caller sizes as\n",
+ " `max_steps * pdtbs * grad_accum * buffer`.\n",
+ " * Index pools are built once at construction from the dataset's\n",
+ " `difficulty` column; only the active pool + cursor are mutated\n",
+ " during iteration, which keeps per-step overhead O(1).\n",
+ " * If the curriculum's current tier has no tasks in the dataset\n",
+ " (mis-configured YAML), we fall back to the union of all tiers so\n",
+ " training doesn't deadlock.\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, dataset, curriculum, num_samples: int) -> None:\n",
+ " self.dataset = dataset\n",
+ " self.curriculum = curriculum\n",
+ " self.num_samples = int(num_samples)\n",
+ " difficulties = list(dataset[\"difficulty\"])\n",
+ " self._pools: dict[str, list[int]] = defaultdict(list)\n",
+ " for idx, diff in enumerate(difficulties):\n",
+ " self._pools[diff].append(idx)\n",
+ " self._all_indices = list(range(len(difficulties)))\n",
+ " log.info(\n",
+ " \"CurriculumTierSampler: num_samples=%d, tier sizes=%s\",\n",
+ " self.num_samples,\n",
+ " {k: len(v) for k, v in self._pools.items()},\n",
+ " )\n",
+ "\n",
+ " def _pool_for(self, tier_value: str) -> list[int]:\n",
+ " pool = self._pools.get(tier_value) or self._all_indices\n",
+ " return list(pool)\n",
+ "\n",
+ " def __len__(self) -> int:\n",
+ " return self.num_samples\n",
+ "\n",
+ " def __iter__(self):\n",
+ " count = 0\n",
+ " cur_tier: str | None = None\n",
+ " pool: list[int] = []\n",
+ " cursor = 0\n",
+ " while count < self.num_samples:\n",
+ " new_tier = self.curriculum.current_difficulty.value\n",
+ " if new_tier != cur_tier:\n",
+ " cur_tier = new_tier\n",
+ " pool = self._pool_for(cur_tier)\n",
+ " random.shuffle(pool)\n",
+ " cursor = 0\n",
+ " if cursor >= len(pool):\n",
+ " random.shuffle(pool)\n",
+ " cursor = 0\n",
+ " yield pool[cursor]\n",
+ " cursor += 1\n",
+ " count += 1\n",
+ "\n",
+ "\n",
+ "class CurriculumPromotionCallback(TrainerCallback):\n",
+ " \"\"\"Observability: log when the curriculum promotes.\"\"\"\n",
+ "\n",
+ " def __init__(self, curriculum) -> None:\n",
+ " self.curriculum = curriculum\n",
+ " self._last_tier: str | None = None\n",
+ "\n",
+ " def on_step_end(self, args, state, control, **kw):\n",
+ " tier = self.curriculum.current_difficulty.value\n",
+ " if tier != self._last_tier:\n",
+ " if self._last_tier is not None:\n",
+ " log.info(\n",
+ " \"[CurriculumPromotionCallback] tier promoted %s -> %s at step %d\",\n",
+ " self._last_tier, tier, state.global_step,\n",
+ " )\n",
+ " self._last_tier = tier\n",
+ "\n",
+ "\n",
+ "class EnvHealthCallback(TrainerCallback):\n",
+ " \"\"\"Log env health counters + drain them every N steps.\n",
+ "\n",
+ " Also provides an early-warning bail-out: if every scoring call in a\n",
+ " window came back as timeout/conn_err, the hosted env is probably down\n",
+ " and we want to stop training rather than polluting the adapter with\n",
+ " zero-reward updates.\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, env_client: EnvRewardClient, probe_every: int = 50,\n",
+ " fail_threshold: int = 32) -> None:\n",
+ " self.env = env_client\n",
+ " self.probe_every = probe_every\n",
+ " self.fail_threshold = fail_threshold\n",
+ "\n",
+ " def on_log(self, args, state, control, logs=None, **kw):\n",
+ " if state.global_step == 0 or state.global_step % self.probe_every != 0:\n",
+ " return\n",
+ " counters = self.env.drain_counters()\n",
+ " log.info(\"[env counters] step=%d %s\", state.global_step, counters)\n",
+ " if counters[\"timeout\"] + counters[\"conn_err\"] >= self.fail_threshold:\n",
+ " log.error(\"[EnvHealthCallback] %s at step %d — stopping training.\",\n",
+ " counters, state.global_step)\n",
+ " control.should_training_stop = True\n",
+ "\n",
+ "\n",
+ "class ProgressLogCallback(TrainerCallback):\n",
+ " \"\"\"Mirror TRL's scalar logs (loss, reward, kl, ...) to the Python logger.\n",
+ "\n",
+ " TRL writes its scalars via `Trainer.log`, which goes to `state.log_history`\n",
+ " and to whichever integrations are listed in `report_to`. With\n",
+ " `report_to=\"none\"` there is no on-screen feedback during training — just\n",
+ " a tqdm bar that Jupyter sometimes hides. This callback hooks `on_log` and\n",
+ " forwards the scalar dict through `log.info` every step, plus a heartbeat\n",
+ " line on `on_step_end` every `heartbeat_every` steps so you always see\n",
+ " *something* even when TRL hasn't emitted a scalar yet (e.g. the opening\n",
+ " generation phase before the first optimizer step).\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(self, heartbeat_every: int = 1, run_label: str = \"\") -> None:\n",
+ " self.heartbeat_every = max(1, int(heartbeat_every))\n",
+ " self.run_label = run_label\n",
+ " self._t_start: float | None = None\n",
+ " self._t_last: float | None = None\n",
+ "\n",
+ " def on_train_begin(self, args, state, control, **kw):\n",
+ " self._t_start = time.time()\n",
+ " self._t_last = self._t_start\n",
+ " log.info(\"%s train_begin | max_steps=%s\", self._tag(), args.max_steps)\n",
+ "\n",
+ " def on_step_end(self, args, state, control, **kw):\n",
+ " if state.global_step % self.heartbeat_every != 0:\n",
+ " return\n",
+ " now = time.time()\n",
+ " dt = now - (self._t_last or now)\n",
+ " self._t_last = now\n",
+ " log.info(\"%s step %d/%s (+%.1fs)\",\n",
+ " self._tag(), state.global_step, args.max_steps, dt)\n",
+ "\n",
+ " def on_log(self, args, state, control, logs=None, **kw):\n",
+ " if not logs:\n",
+ " return\n",
+ " # Drop noisy / non-scalar fields and round for legibility.\n",
+ " scalars = {k: (round(v, 4) if isinstance(v, float) else v)\n",
+ " for k, v in logs.items()\n",
+ " if isinstance(v, (int, float))}\n",
+ " if scalars:\n",
+ " log.info(\"%s log step=%d %s\",\n",
+ " self._tag(), state.global_step, scalars)\n",
+ "\n",
+ " def on_train_end(self, args, state, control, **kw):\n",
+ " elapsed = time.time() - (self._t_start or time.time())\n",
+ " log.info(\"%s train_end | global_step=%d elapsed=%.1fs\",\n",
+ " self._tag(), state.global_step, elapsed)\n",
+ "\n",
+ " def _tag(self) -> str:\n",
+ " return f\"[{self.run_label}]\" if self.run_label else \"[train]\"\n",
+ "\n",
+ "\n",
+ "def build_trainer(model, tokenizer, train_ds, eval_ds,\n",
+ " reward_funcs: Iterable[Callable],\n",
+ " cfg: TrainingConfig, *,\n",
+ " output_dir: str, run_name: str,\n",
+ " use_fp16: bool, use_bf16: bool,\n",
+ " max_steps: int | None = None,\n",
+ " save_strategy: str = \"steps\",\n",
+ " eval_strategy: str = \"steps\",\n",
+ " curriculum=None,\n",
+ " num_samples: int | None = None) -> GRPOTrainer:\n",
+ " \"\"\"Assemble a GRPOTrainer from a typed TrainingConfig.\n",
+ "\n",
+ " When `curriculum` is supplied, the trainer's train sampler is\n",
+ " replaced with a CurriculumTierSampler that yields dataset indices\n",
+ " from the curriculum's current tier — reactive to promotion. The\n",
+ " dataset passed in as `train_ds` must carry a `difficulty` column\n",
+ " (use make_full_curriculum_dataset). `num_samples` sizes the\n",
+ " sampler; callers typically set it to\n",
+ " `max_steps * per_device_train_batch_size * gradient_accumulation_steps * 1.2`.\n",
+ " \"\"\"\n",
+ " args = GRPOConfig(\n",
+ " output_dir=output_dir, run_name=run_name,\n",
+ " num_generations=cfg.num_generations, beta=cfg.beta,\n",
+ " temperature=cfg.temperature, top_p=cfg.top_p,\n",
+ " max_prompt_length=cfg.max_prompt_length,\n",
+ " max_completion_length=cfg.max_completion_length,\n",
+ " learning_rate=cfg.learning_rate, lr_scheduler_type=\"cosine\",\n",
+ " optim=\"adamw_8bit\", weight_decay=0.0, max_grad_norm=1.0,\n",
+ " warmup_ratio=cfg.warmup_ratio,\n",
+ " per_device_train_batch_size=cfg.per_device_train_batch_size,\n",
+ " # TRL's GRPOConfig asserts per_device_eval_batch_size * num_processes is\n",
+ " # divisible by num_generations (one eval prompt produces G completions;\n",
+ " # anything smaller can't form a group). Defaulting it to num_generations\n",
+ " # is the smallest value that satisfies the check on a single-process\n",
+ " # setup — matches how GRPOTrainer batches eval internally.\n",
+ " per_device_eval_batch_size=cfg.num_generations,\n",
+ " gradient_accumulation_steps=cfg.gradient_accumulation_steps,\n",
+ " num_train_epochs=cfg.num_train_epochs,\n",
+ " max_steps=(max_steps if max_steps is not None else -1),\n",
+ " fp16=use_fp16, bf16=use_bf16,\n",
+ " # Mid-training eval is disabled for GRPO: TRL's prediction_step\n",
+ " # calls _generate_and_score_completions, which reshapes rewards\n",
+ " # as (-1, num_generations). At eval time the effective per-prompt\n",
+ " # completion count can differ from num_generations, so the view\n",
+ " # errors with \"shape '[-1, G]' is invalid for input of size N\".\n",
+ " # The notebook runs its own before/after eval via evaluate_single_step,\n",
+ " # so we lose nothing by skipping TRL's eval loop here.\n",
+ " eval_strategy=eval_strategy, eval_steps=cfg.eval_steps,\n",
+ " save_strategy=save_strategy, save_steps=cfg.save_steps,\n",
+ " save_total_limit=cfg.save_total_limit,\n",
+ " logging_steps=1, report_to=\"none\", seed=cfg.seed,\n",
+ " remove_unused_columns=False, # CRITICAL: preserves task_id for reward_fns\n",
+ " disable_tqdm=False,\n",
+ " )\n",
+ " callbacks = [\n",
+ " EnvHealthCallback(ENV_CLIENT),\n",
+ " ProgressLogCallback(heartbeat_every=1, run_label=run_name),\n",
+ " ]\n",
+ " if curriculum is not None:\n",
+ " callbacks.append(CurriculumPromotionCallback(curriculum))\n",
+ "\n",
+ " trainer = GRPOTrainer(\n",
+ " model=model, processing_class=tokenizer,\n",
+ " reward_funcs=list(reward_funcs),\n",
+ " reward_weights=[1.0, 0.15, 0.05],\n",
+ " args=args, train_dataset=train_ds, eval_dataset=eval_ds,\n",
+ " callbacks=callbacks,\n",
+ " )\n",
+ "\n",
+ " if curriculum is not None:\n",
+ " if num_samples is None:\n",
+ " raise ValueError(\"num_samples is required when curriculum is set\")\n",
+ " if \"difficulty\" not in train_ds.column_names:\n",
+ " raise ValueError(\n",
+ " \"curriculum-driven training needs a `difficulty` column on \"\n",
+ " \"train_ds — use make_full_curriculum_dataset().\"\n",
+ " )\n",
+ " _sampler = CurriculumTierSampler(\n",
+ " dataset=train_ds, curriculum=curriculum, num_samples=num_samples,\n",
+ " )\n",
+ " # Monkey-patch the trainer's sampler factory so the DataLoader,\n",
+ " # which is built lazily inside trainer.train(), pulls from our\n",
+ " # tier-reactive sampler instead of the default RandomSampler.\n",
+ " # Using MethodType keeps `self` binding correct on the bound call.\n",
+ " import types\n",
+ " def _curriculum_train_sampler(self):\n",
+ " return _sampler\n",
+ " trainer._get_train_sampler = types.MethodType(\n",
+ " _curriculum_train_sampler, trainer,\n",
+ " )\n",
+ "\n",
+ " return trainer\n",
+ "\n",
+ "\n",
+ "log.info(\"GRPOTrainer factory ready.\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5a29cad6",
+ "metadata": {
+ "id": "5a29cad6"
+ },
+ "source": [
+ "## 12 · Optuna hyperparameter search\n",
+ "\n",
+ "Six short trials (30 GRPO steps each, 80-row training subset). Each trial returns the mean **env reward on the held-out val set** — the one metric we actually want to maximize.\n",
+ "\n",
+ "- **Resumable**: sqlite storage + `load_if_exists=True`. A dropped Colab session picks up where it left off.\n",
+ "- **Pruned**: `MedianPruner` — kill trials that are trending below the median after 10 steps.\n",
+ "- **Search space** chosen to bracket the GRPO-on-3B defaults reported in the DeepSeek-Math paper."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "0b184374",
+ "metadata": {
+ "id": "0b184374",
+ "outputId": "11dc0d5c-9899-4aa1-ee6d-749e14ea3a04",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[I 2026-04-25 14:56:34,668] A new study created in RDB with name: aws-rl-grpo-search\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Optuna study 'aws-rl-grpo-search': 0 completed, 4 remaining.\n",
+ "14:56:34 | INFO | grpo | [trial 0] starting | cfg={'lr': 4.328450221293881e-06, 'beta': 0.09507143064099162, 'temp': 0.9195981825434215}\n",
+ "14:56:34 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "14:56:34 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "14:56:34 | INFO | grpo | [trial 0] loading SFT policy (4-bit Qwen2.5-Coder-3B)...\n",
+ "==((====))== Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 4.57.6.\n",
+ " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n",
+ "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.35. FA2 = False]\n",
+ " \"-____-\" Free license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
+ "14:56:52 | INFO | grpo | [trial 0] policy loaded in 17.9s\n",
+ "14:56:52 | INFO | grpo | CurriculumTierSampler: num_samples=192, tier sizes={'warmup': 2, 'beginner': 2, 'intermediate': 2, 'advanced': 3, 'expert': 3}\n",
+ "14:56:52 | INFO | grpo | [trial 0] starting train() for 10 steps...\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
+ " \\\\ /| Num examples = 12 | Num Epochs = 5 | Total steps = 10\n",
+ "O^O/ \\_/ \\ Batch size per device = 2 | Gradient accumulation steps = 8\n",
+ "\\ / Data Parallel GPUs = 1 | Total batch size (2 x 8 x 1) = 16\n",
+ " \"-____-\" Trainable parameters = 7,372,800 of 3,093,311,488 (0.24% trained)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14:56:55 | INFO | grpo | [optuna-trial-0] train_begin | max_steps=10\n",
+ "Unsloth: Will smartly offload gradients to save VRAM!\n",
+ "14:59:00 | INFO | server.services.curriculum | Episode 1: task=33 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "14:59:00 | INFO | server.services.curriculum | Episode 2: task=37 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "14:59:43 | INFO | grpo | [optuna-trial-0] step 1/10 (+168.1s)\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
\n",
+ " [10/10 16:25, Epoch 5/5]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "14:59:43 | INFO | grpo | [optuna-trial-0] log step=1 {'loss': -0.0219, 'grad_norm': 0.2647, 'learning_rate': 0.0, 'num_tokens': 3329.0, 'completions/mean_length': 42.0625, 'completions/min_length': 24.0, 'completions/max_length': 54.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 42.0625, 'completions/min_terminated_length': 24.0, 'completions/max_terminated_length': 54.0, 'rewards/env_reward/mean': 0.9375, 'rewards/env_reward/std': 0.25, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.9375, 'reward_std': 0.1768, 'frac_reward_zero_std': 0.5, 'completion_length': 42.0625, 'kl': 0.1109, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.6667}\n",
+ "15:01:26 | INFO | server.services.curriculum | Loaded 25 beginner tasks total\n",
+ "15:01:26 | INFO | server.services.curriculum | PROMOTED from warmup to beginner (rate=1.00, FAST-TRACK)\n",
+ "15:01:26 | INFO | server.services.curriculum | Episode 3: task=33 difficulty=warmup achieved=True tier_rate=0.00\n",
+ "15:01:26 | INFO | server.services.curriculum | Episode 4: task=37 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "15:01:29 | INFO | grpo | [optuna-trial-0] step 2/10 (+106.0s)\n",
+ "15:01:29 | INFO | grpo | [CurriculumPromotionCallback] tier promoted warmup -> beginner at step 2\n",
+ "15:01:29 | INFO | grpo | [optuna-trial-0] log step=2 {'loss': -0.009, 'grad_norm': 0.5444, 'learning_rate': 0.0, 'num_tokens': 6695.0, 'completions/mean_length': 44.375, 'completions/min_length': 33.0, 'completions/max_length': 64.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 44.375, 'completions/min_terminated_length': 33.0, 'completions/max_terminated_length': 64.0, 'rewards/env_reward/mean': 0.75, 'rewards/env_reward/std': 0.4472, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.75, 'reward_std': 0.4356, 'frac_reward_zero_std': 0.0, 'completion_length': 42.375, 'kl': 0.0906, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 1.0}\n",
+ "15:02:10 | INFO | server.services.curriculum | Episode 5: task=8 difficulty=beginner achieved=True tier_rate=1.00\n",
+ "15:02:10 | INFO | server.services.curriculum | Loaded 25 intermediate tasks total\n",
+ "15:02:10 | INFO | server.services.curriculum | PROMOTED from beginner to intermediate (rate=1.00, FAST-TRACK)\n",
+ "15:02:10 | INFO | server.services.curriculum | Episode 6: task=53 difficulty=beginner achieved=True tier_rate=0.00\n",
+ "15:02:13 | INFO | grpo | [optuna-trial-0] step 3/10 (+44.4s)\n",
+ "15:02:13 | INFO | grpo | [CurriculumPromotionCallback] tier promoted beginner -> intermediate at step 3\n",
+ "15:02:13 | INFO | grpo | [optuna-trial-0] log step=3 {'loss': -0.1569, 'grad_norm': 0.1853, 'learning_rate': 0.0, 'completion_length': 48.25, 'kl': 0.1154, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 10256.0, 'completions/mean_length': 51.5625, 'completions/min_length': 29.0, 'completions/max_length': 72.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 51.5625, 'completions/min_terminated_length': 29.0, 'completions/max_terminated_length': 72.0, 'rewards/env_reward/mean': 0.875, 'rewards/env_reward/std': 0.3416, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.875, 'reward_std': 0.2315, 'frac_reward_zero_std': 0.5, 'epoch': 1.6667}\n",
+ "15:02:16 | INFO | grpo | [optuna-trial-0] step 4/10 (+3.1s)\n",
+ "15:02:17 | INFO | grpo | [optuna-trial-0] log step=4 {'loss': 0.2385, 'grad_norm': 0.3908, 'learning_rate': 0.0, 'completion_length': 53.0, 'kl': 0.1076, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 2.0}\n",
+ "15:03:37 | INFO | server.services.curriculum | Episode 7: task=11 difficulty=intermediate achieved=True tier_rate=1.00\n",
+ "15:03:37 | INFO | server.services.curriculum | Episode 8: task=84 difficulty=intermediate achieved=True tier_rate=1.00\n",
+ "15:03:45 | INFO | grpo | [optuna-trial-0] step 5/10 (+88.1s)\n",
+ "15:03:45 | INFO | grpo | [optuna-trial-0] log step=5 {'loss': -0.0531, 'grad_norm': 0.3091, 'learning_rate': 0.0, 'num_tokens': 14343.0, 'completions/mean_length': 71.4375, 'completions/min_length': 47.0, 'completions/max_length': 124.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 71.4375, 'completions/min_terminated_length': 47.0, 'completions/max_terminated_length': 124.0, 'rewards/env_reward/mean': 0.8125, 'rewards/env_reward/std': 0.4031, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.8125, 'reward_std': 0.4082, 'frac_reward_zero_std': 0.0, 'completion_length': 71.4375, 'kl': 0.2469, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 2.6667}\n",
+ "15:04:56 | INFO | server.services.curriculum | Loaded 25 advanced tasks total\n",
+ "15:04:56 | INFO | server.services.curriculum | PROMOTED from intermediate to advanced (rate=1.00, FAST-TRACK)\n",
+ "15:04:56 | INFO | server.services.curriculum | Episode 9: task=84 difficulty=intermediate achieved=True tier_rate=0.00\n",
+ "15:04:56 | INFO | server.services.curriculum | Episode 10: task=11 difficulty=intermediate achieved=True tier_rate=1.00\n",
+ "15:04:59 | INFO | grpo | [optuna-trial-0] step 6/10 (+74.5s)\n",
+ "15:04:59 | INFO | grpo | [CurriculumPromotionCallback] tier promoted intermediate -> advanced at step 6\n",
+ "15:04:59 | INFO | grpo | [optuna-trial-0] log step=6 {'loss': 0.0077, 'grad_norm': 0.4035, 'learning_rate': 0.0, 'num_tokens': 18336.0, 'completions/mean_length': 65.5625, 'completions/min_length': 45.0, 'completions/max_length': 102.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 65.5625, 'completions/min_terminated_length': 45.0, 'completions/max_terminated_length': 102.0, 'rewards/env_reward/mean': 0.875, 'rewards/env_reward/std': 0.3416, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9985, 'rewards/length_reward/std': 0.0058, 'reward': 2.8735, 'reward_std': 0.353, 'frac_reward_zero_std': 0.0, 'completion_length': 68.375, 'kl': 0.2219, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 3.0}\n",
+ "15:08:41 | INFO | server.services.curriculum | Episode 11: task=95 difficulty=advanced achieved=True tier_rate=1.00\n",
+ "15:08:41 | INFO | server.services.curriculum | Episode 12: task=97 difficulty=advanced achieved=False tier_rate=0.61\n",
+ "15:08:41 | INFO | server.services.curriculum | Episode 13: task=96 difficulty=advanced achieved=False tier_rate=0.42\n",
+ "15:08:45 | INFO | grpo | [optuna-trial-0] step 7/10 (+226.0s)\n",
+ "15:08:45 | INFO | grpo | [optuna-trial-0] log step=7 {'loss': -0.1714, 'grad_norm': 0.2604, 'learning_rate': 0.0, 'completion_length': 73.0, 'kl': 0.2541, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 23311.0, 'completions/mean_length': 85.5625, 'completions/min_length': 41.0, 'completions/max_length': 135.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 85.5625, 'completions/min_terminated_length': 41.0, 'completions/max_terminated_length': 135.0, 'rewards/env_reward/mean': 0.6746, 'rewards/env_reward/std': 0.2689, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9403, 'rewards/length_reward/std': 0.0799, 'reward': 2.6149, 'reward_std': 0.3057, 'frac_reward_zero_std': 0.0, 'epoch': 3.6667}\n",
+ "15:08:49 | INFO | grpo | [optuna-trial-0] step 8/10 (+4.1s)\n",
+ "15:08:49 | INFO | grpo | [optuna-trial-0] log step=8 {'loss': 0.5884, 'grad_norm': 0.2926, 'learning_rate': 0.0, 'completion_length': 87.875, 'kl': 0.1595, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.0}\n",
+ "15:12:13 | INFO | server.services.curriculum | Episode 14: task=96 difficulty=advanced achieved=False tier_rate=0.31\n",
+ "15:12:13 | INFO | server.services.curriculum | Episode 15: task=97 difficulty=advanced achieved=False tier_rate=0.23\n",
+ "15:12:13 | INFO | server.services.curriculum | Episode 16: task=95 difficulty=advanced achieved=True tier_rate=0.40\n",
+ "15:12:21 | INFO | grpo | [optuna-trial-0] step 9/10 (+212.1s)\n",
+ "15:12:21 | INFO | grpo | [optuna-trial-0] log step=9 {'loss': 0.0684, 'grad_norm': 0.2102, 'learning_rate': 0.0, 'num_tokens': 28312.0, 'completions/mean_length': 87.1875, 'completions/min_length': 50.0, 'completions/max_length': 114.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 87.1875, 'completions/min_terminated_length': 50.0, 'completions/max_terminated_length': 114.0, 'rewards/env_reward/mean': 0.6315, 'rewards/env_reward/std': 0.2486, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9085, 'rewards/length_reward/std': 0.0836, 'reward': 2.5399, 'reward_std': 0.234, 'frac_reward_zero_std': 0.0, 'completion_length': 87.1875, 'kl': 0.1919, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.6667}\n",
+ "15:16:04 | INFO | server.services.curriculum | Episode 17: task=96 difficulty=advanced achieved=False tier_rate=0.32\n",
+ "15:16:04 | INFO | server.services.curriculum | Episode 18: task=97 difficulty=advanced achieved=False tier_rate=0.26\n",
+ "15:16:04 | INFO | server.services.curriculum | Task 95 GRADUATED (rate=1.00) — scheduling spaced repetition\n",
+ "15:16:04 | INFO | server.services.curriculum | Episode 19: task=95 difficulty=advanced achieved=True tier_rate=0.40\n",
+ "15:16:09 | INFO | grpo | [optuna-trial-0] step 10/10 (+227.5s)\n",
+ "15:16:09 | INFO | grpo | [optuna-trial-0] log step=10 {'loss': 0.1819, 'grad_norm': 0.255, 'learning_rate': 0.0, 'num_tokens': 33437.0, 'completions/mean_length': 95.1875, 'completions/min_length': 52.0, 'completions/max_length': 157.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 95.1875, 'completions/min_terminated_length': 52.0, 'completions/max_terminated_length': 157.0, 'rewards/env_reward/mean': 0.6533, 'rewards/env_reward/std': 0.2474, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9344, 'rewards/length_reward/std': 0.079, 'reward': 2.5877, 'reward_std': 0.2937, 'frac_reward_zero_std': 0.0, 'completion_length': 99.0, 'kl': 0.1798, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 5.0}\n",
+ "15:16:09 | INFO | grpo | [optuna-trial-0] log step=10 {'train_runtime': 1153.87, 'train_samples_per_second': 0.139, 'train_steps_per_second': 0.009, 'total_flos': 0.0, 'train_loss': 0.0673, 'epoch': 5.0}\n",
+ "15:16:09 | INFO | grpo | [optuna-trial-0] train_end | global_step=10 elapsed=1153.9s\n",
+ "15:16:10 | INFO | grpo | [trial 0] train() finished in 1157.6s\n",
+ "15:16:10 | INFO | grpo | [trial 0] running single-step eval on 12 tasks...\n",
+ "15:17:24 | INFO | grpo | [trial 0] eval finished in 74.1s\n",
+ "15:17:24 | INFO | grpo | [trial 0] DONE | env_reward_mean=0.4727 success=0.250 tier=advanced graduated=1\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[I 2026-04-25 15:17:27,865] Trial 0 finished with value: 0.4726666666666667 and parameters: {'learning_rate': 4.328450221293881e-06, 'beta': 0.09507143064099162, 'temperature': 0.9195981825434215}. Best is trial 0 with value: 0.4726666666666667.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "15:17:27 | INFO | grpo | [trial 1] starting | cfg={'lr': 1.0401663679887314e-05, 'beta': 0.015601864044243652, 'temp': 0.7467983561008608}\n",
+ "15:17:27 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "15:17:27 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "15:17:27 | INFO | grpo | [trial 1] loading SFT policy (4-bit Qwen2.5-Coder-3B)...\n",
+ "==((====))== Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 4.57.6.\n",
+ " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n",
+ "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.35. FA2 = False]\n",
+ " \"-____-\" Free license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
+ "15:17:46 | INFO | grpo | [trial 1] policy loaded in 18.6s\n",
+ "15:17:46 | INFO | grpo | CurriculumTierSampler: num_samples=192, tier sizes={'warmup': 2, 'beginner': 2, 'intermediate': 2, 'advanced': 3, 'expert': 3}\n",
+ "15:17:46 | INFO | grpo | [trial 1] starting train() for 10 steps...\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
+ " \\\\ /| Num examples = 12 | Num Epochs = 5 | Total steps = 10\n",
+ "O^O/ \\_/ \\ Batch size per device = 2 | Gradient accumulation steps = 8\n",
+ "\\ / Data Parallel GPUs = 1 | Total batch size (2 x 8 x 1) = 16\n",
+ " \"-____-\" Trainable parameters = 7,372,800 of 3,093,311,488 (0.24% trained)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "15:17:51 | INFO | grpo | [optuna-trial-1] train_begin | max_steps=10\n",
+ "Unsloth: Will smartly offload gradients to save VRAM!\n",
+ "15:19:25 | INFO | server.services.curriculum | Episode 1: task=33 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "15:19:25 | INFO | server.services.curriculum | Episode 2: task=37 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "15:19:35 | INFO | grpo | [optuna-trial-1] step 1/10 (+104.0s)\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
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+ " [10/10 15:26, Epoch 5/5]\n",
+ "
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+ " \n",
+ " \n",
+ " | Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ "
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+ ]
+ },
+ "metadata": {}
+ },
+ {
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+ "text": [
+ "15:19:35 | INFO | grpo | [optuna-trial-1] log step=1 {'loss': -0.0156, 'grad_norm': 0.4741, 'learning_rate': 0.0, 'num_tokens': 3281.0, 'completions/mean_length': 39.0625, 'completions/min_length': 24.0, 'completions/max_length': 52.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 39.0625, 'completions/min_terminated_length': 24.0, 'completions/max_terminated_length': 52.0, 'rewards/env_reward/mean': 0.8125, 'rewards/env_reward/std': 0.4031, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.8125, 'reward_std': 0.4082, 'frac_reward_zero_std': 0.0, 'completion_length': 39.0625, 'kl': 0.1399, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.6667}\n",
+ "15:20:00 | INFO | server.services.curriculum | Loaded 25 beginner tasks total\n",
+ "15:20:00 | INFO | server.services.curriculum | PROMOTED from warmup to beginner (rate=1.00, FAST-TRACK)\n",
+ "15:20:00 | INFO | server.services.curriculum | Episode 3: task=37 difficulty=warmup achieved=True tier_rate=0.00\n",
+ "15:20:00 | INFO | server.services.curriculum | Episode 4: task=33 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "15:20:03 | INFO | grpo | [optuna-trial-1] step 2/10 (+28.3s)\n",
+ "15:20:03 | INFO | grpo | [CurriculumPromotionCallback] tier promoted warmup -> beginner at step 2\n",
+ "15:20:03 | INFO | grpo | [optuna-trial-1] log step=2 {'loss': -0.1774, 'grad_norm': 0.353, 'learning_rate': 0.0, 'num_tokens': 6596.0, 'completions/mean_length': 41.1875, 'completions/min_length': 28.0, 'completions/max_length': 64.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 41.1875, 'completions/min_terminated_length': 28.0, 'completions/max_terminated_length': 64.0, 'rewards/env_reward/mean': 0.8125, 'rewards/env_reward/std': 0.4031, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.8125, 'reward_std': 0.4082, 'frac_reward_zero_std': 0.0, 'completion_length': 43.625, 'kl': 0.1407, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 1.0}\n",
+ "15:20:42 | INFO | server.services.curriculum | Episode 5: task=8 difficulty=beginner achieved=True tier_rate=1.00\n",
+ "15:20:42 | INFO | server.services.curriculum | Loaded 25 intermediate tasks total\n",
+ "15:20:42 | INFO | server.services.curriculum | PROMOTED from beginner to intermediate (rate=1.00, FAST-TRACK)\n",
+ "15:20:42 | INFO | server.services.curriculum | Episode 6: task=53 difficulty=beginner achieved=True tier_rate=0.00\n",
+ "15:20:45 | INFO | grpo | [optuna-trial-1] step 3/10 (+41.9s)\n",
+ "15:20:45 | INFO | grpo | [CurriculumPromotionCallback] tier promoted beginner -> intermediate at step 3\n",
+ "15:20:45 | INFO | grpo | [optuna-trial-1] log step=3 {'loss': 0.1491, 'grad_norm': 0.4402, 'learning_rate': 0.0, 'completion_length': 42.3125, 'kl': 0.1222, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 10133.0, 'completions/mean_length': 50.0625, 'completions/min_length': 32.0, 'completions/max_length': 71.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 50.0625, 'completions/min_terminated_length': 32.0, 'completions/max_terminated_length': 71.0, 'rewards/env_reward/mean': 0.9375, 'rewards/env_reward/std': 0.25, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.9375, 'reward_std': 0.1768, 'frac_reward_zero_std': 0.5, 'epoch': 1.6667}\n",
+ "15:20:48 | INFO | grpo | [optuna-trial-1] step 4/10 (+3.1s)\n",
+ "15:20:48 | INFO | grpo | [optuna-trial-1] log step=4 {'loss': -0.1253, 'grad_norm': 0.0936, 'learning_rate': 0.0, 'completion_length': 54.25, 'kl': 0.0941, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 2.0}\n",
+ "15:22:06 | INFO | server.services.curriculum | Episode 7: task=84 difficulty=intermediate achieved=True tier_rate=1.00\n",
+ "15:22:06 | INFO | server.services.curriculum | Episode 8: task=11 difficulty=intermediate achieved=True tier_rate=1.00\n",
+ "15:22:13 | INFO | grpo | [optuna-trial-1] step 5/10 (+85.2s)\n",
+ "15:22:13 | INFO | grpo | [optuna-trial-1] log step=5 {'loss': 0.0238, 'grad_norm': 0.2486, 'learning_rate': 0.0, 'num_tokens': 14299.0, 'completions/mean_length': 76.375, 'completions/min_length': 41.0, 'completions/max_length': 120.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 76.375, 'completions/min_terminated_length': 41.0, 'completions/max_terminated_length': 120.0, 'rewards/env_reward/mean': 0.75, 'rewards/env_reward/std': 0.4472, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.75, 'reward_std': 0.2673, 'frac_reward_zero_std': 0.5, 'completion_length': 76.375, 'kl': 0.2785, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 2.6667}\n",
+ "15:23:23 | INFO | server.services.curriculum | Loaded 25 advanced tasks total\n",
+ "15:23:23 | INFO | server.services.curriculum | PROMOTED from intermediate to advanced (rate=1.00, FAST-TRACK)\n",
+ "15:23:23 | INFO | server.services.curriculum | Episode 9: task=84 difficulty=intermediate achieved=True tier_rate=0.00\n",
+ "15:23:23 | INFO | server.services.curriculum | Episode 10: task=11 difficulty=intermediate achieved=True tier_rate=1.00\n",
+ "15:23:27 | INFO | grpo | [optuna-trial-1] step 6/10 (+73.4s)\n",
+ "15:23:27 | INFO | grpo | [CurriculumPromotionCallback] tier promoted intermediate -> advanced at step 6\n",
+ "15:23:27 | INFO | grpo | [optuna-trial-1] log step=6 {'loss': 0.4783, 'grad_norm': 0.742, 'learning_rate': 0.0, 'num_tokens': 18486.0, 'completions/mean_length': 77.6875, 'completions/min_length': 39.0, 'completions/max_length': 149.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 77.6875, 'completions/min_terminated_length': 39.0, 'completions/max_terminated_length': 149.0, 'rewards/env_reward/mean': 0.875, 'rewards/env_reward/std': 0.3416, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.875, 'reward_std': 0.3536, 'frac_reward_zero_std': 0.0, 'completion_length': 71.875, 'kl': 0.2911, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 3.0}\n",
+ "15:27:13 | INFO | server.services.curriculum | Episode 11: task=95 difficulty=advanced achieved=True tier_rate=1.00\n",
+ "15:27:13 | INFO | server.services.curriculum | Episode 12: task=96 difficulty=advanced achieved=False tier_rate=0.61\n",
+ "15:27:13 | INFO | server.services.curriculum | Episode 13: task=97 difficulty=advanced achieved=False tier_rate=0.42\n",
+ "15:27:17 | INFO | grpo | [optuna-trial-1] step 7/10 (+230.2s)\n",
+ "15:27:17 | INFO | grpo | [optuna-trial-1] log step=7 {'loss': -0.3887, 'grad_norm': 0.1856, 'learning_rate': 0.0, 'completion_length': 82.9375, 'kl': 0.2783, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 23490.0, 'completions/mean_length': 87.625, 'completions/min_length': 50.0, 'completions/max_length': 138.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 87.625, 'completions/min_terminated_length': 50.0, 'completions/max_terminated_length': 138.0, 'rewards/env_reward/mean': 0.571, 'rewards/env_reward/std': 0.2182, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9311, 'rewards/length_reward/std': 0.0807, 'reward': 2.5022, 'reward_std': 0.2092, 'frac_reward_zero_std': 0.0, 'epoch': 3.6667}\n",
+ "15:27:21 | INFO | grpo | [optuna-trial-1] step 8/10 (+4.1s)\n",
+ "15:27:21 | INFO | grpo | [optuna-trial-1] log step=8 {'loss': 0.4028, 'grad_norm': 0.2385, 'learning_rate': 0.0, 'completion_length': 92.875, 'kl': 0.1724, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.0}\n",
+ "15:31:04 | INFO | server.services.curriculum | Episode 14: task=97 difficulty=advanced achieved=False tier_rate=0.31\n",
+ "15:31:04 | INFO | server.services.curriculum | Episode 15: task=95 difficulty=advanced achieved=True tier_rate=0.47\n",
+ "15:31:04 | INFO | server.services.curriculum | Episode 16: task=96 difficulty=advanced achieved=False tier_rate=0.37\n",
+ "15:31:13 | INFO | grpo | [optuna-trial-1] step 9/10 (+231.8s)\n",
+ "15:31:13 | INFO | grpo | [optuna-trial-1] log step=9 {'loss': 0.0169, 'grad_norm': 0.1892, 'learning_rate': 0.0, 'num_tokens': 28516.0, 'completions/mean_length': 88.75, 'completions/min_length': 52.0, 'completions/max_length': 137.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 88.75, 'completions/min_terminated_length': 52.0, 'completions/max_terminated_length': 137.0, 'rewards/env_reward/mean': 0.6006, 'rewards/env_reward/std': 0.2428, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9484, 'rewards/length_reward/std': 0.0792, 'reward': 2.5491, 'reward_std': 0.2824, 'frac_reward_zero_std': 0.0, 'completion_length': 88.75, 'kl': 0.2088, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.6667}\n",
+ "15:34:57 | INFO | server.services.curriculum | Episode 17: task=96 difficulty=advanced achieved=False tier_rate=0.29\n",
+ "15:34:57 | INFO | server.services.curriculum | Task 95 GRADUATED (rate=1.00) — scheduling spaced repetition\n",
+ "15:34:57 | INFO | server.services.curriculum | Episode 18: task=95 difficulty=advanced achieved=True tier_rate=0.43\n",
+ "15:34:57 | INFO | server.services.curriculum | Episode 19: task=97 difficulty=advanced achieved=False tier_rate=0.35\n",
+ "15:35:01 | INFO | grpo | [optuna-trial-1] step 10/10 (+228.8s)\n",
+ "15:35:01 | INFO | grpo | [optuna-trial-1] log step=10 {'loss': -0.0104, 'grad_norm': 0.341, 'learning_rate': 0.0, 'num_tokens': 33471.0, 'completions/mean_length': 84.5625, 'completions/min_length': 46.0, 'completions/max_length': 127.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 84.5625, 'completions/min_terminated_length': 46.0, 'completions/max_terminated_length': 127.0, 'rewards/env_reward/mean': 0.6242, 'rewards/env_reward/std': 0.224, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9379, 'rewards/length_reward/std': 0.0828, 'reward': 2.5621, 'reward_std': 0.2517, 'frac_reward_zero_std': 0.0, 'completion_length': 85.0, 'kl': 0.1966, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 5.0}\n",
+ "15:35:01 | INFO | grpo | [optuna-trial-1] log step=10 {'train_runtime': 1030.7831, 'train_samples_per_second': 0.155, 'train_steps_per_second': 0.01, 'total_flos': 0.0, 'train_loss': 0.0353, 'epoch': 5.0}\n",
+ "15:35:01 | INFO | grpo | [optuna-trial-1] train_end | global_step=10 elapsed=1030.8s\n",
+ "15:35:03 | INFO | grpo | [trial 1] train() finished in 1036.9s\n",
+ "15:35:03 | INFO | grpo | [trial 1] running single-step eval on 12 tasks...\n",
+ "15:36:20 | INFO | grpo | [trial 1] eval finished in 76.5s\n",
+ "15:36:20 | INFO | grpo | [trial 1] DONE | env_reward_mean=0.4687 success=0.250 tier=advanced graduated=1\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[I 2026-04-25 15:36:23,361] Trial 1 finished with value: 0.4686666666666667 and parameters: {'learning_rate': 1.0401663679887314e-05, 'beta': 0.015601864044243652, 'temperature': 0.7467983561008608}. Best is trial 0 with value: 0.4726666666666667.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "15:36:23 | INFO | grpo | [trial 2] starting | cfg={'lr': 1.255111517297383e-06, 'beta': 0.08661761457749352, 'temp': 0.8803345035229626}\n",
+ "15:36:23 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "15:36:23 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "15:36:23 | INFO | grpo | [trial 2] loading SFT policy (4-bit Qwen2.5-Coder-3B)...\n",
+ "==((====))== Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 4.57.6.\n",
+ " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n",
+ "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.35. FA2 = False]\n",
+ " \"-____-\" Free license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
+ "15:36:42 | INFO | grpo | [trial 2] policy loaded in 19.0s\n",
+ "15:36:42 | INFO | grpo | CurriculumTierSampler: num_samples=192, tier sizes={'warmup': 2, 'beginner': 2, 'intermediate': 2, 'advanced': 3, 'expert': 3}\n",
+ "15:36:42 | INFO | grpo | [trial 2] starting train() for 10 steps...\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
+ " \\\\ /| Num examples = 12 | Num Epochs = 5 | Total steps = 10\n",
+ "O^O/ \\_/ \\ Batch size per device = 2 | Gradient accumulation steps = 8\n",
+ "\\ / Data Parallel GPUs = 1 | Total batch size (2 x 8 x 1) = 16\n",
+ " \"-____-\" Trainable parameters = 7,372,800 of 3,093,311,488 (0.24% trained)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "15:36:46 | INFO | grpo | [optuna-trial-2] train_begin | max_steps=10\n",
+ "Unsloth: Will smartly offload gradients to save VRAM!\n",
+ "15:37:10 | INFO | server.services.curriculum | Episode 1: task=33 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "15:37:10 | INFO | server.services.curriculum | Episode 2: task=37 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "15:37:19 | INFO | grpo | [optuna-trial-2] step 1/10 (+32.4s)\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ " [10/10 16:00, Epoch 5/5]\n",
+ "
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+ " \n",
+ " \n",
+ " | Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ "
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+ " \n",
+ " \n",
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+ ]
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+ "15:37:19 | INFO | grpo | [optuna-trial-2] log step=1 {'loss': -0.0615, 'grad_norm': 0.4482, 'learning_rate': 0.0, 'num_tokens': 3294.0, 'completions/mean_length': 39.875, 'completions/min_length': 24.0, 'completions/max_length': 54.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 39.875, 'completions/min_terminated_length': 24.0, 'completions/max_terminated_length': 54.0, 'rewards/env_reward/mean': 0.875, 'rewards/env_reward/std': 0.3416, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.875, 'reward_std': 0.3536, 'frac_reward_zero_std': 0.0, 'completion_length': 39.875, 'kl': 0.1186, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.6667}\n",
+ "15:37:45 | INFO | server.services.curriculum | Loaded 25 beginner tasks total\n",
+ "15:37:45 | INFO | server.services.curriculum | PROMOTED from warmup to beginner (rate=1.00, FAST-TRACK)\n",
+ "15:37:45 | INFO | server.services.curriculum | Episode 3: task=37 difficulty=warmup achieved=True tier_rate=0.00\n",
+ "15:37:45 | INFO | server.services.curriculum | Episode 4: task=33 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "15:37:48 | INFO | grpo | [optuna-trial-2] step 2/10 (+29.2s)\n",
+ "15:37:48 | INFO | grpo | [CurriculumPromotionCallback] tier promoted warmup -> beginner at step 2\n",
+ "15:37:48 | INFO | grpo | [optuna-trial-2] log step=2 {'loss': -0.2559, 'grad_norm': 0.6131, 'learning_rate': 0.0, 'num_tokens': 6624.0, 'completions/mean_length': 42.125, 'completions/min_length': 25.0, 'completions/max_length': 61.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 42.125, 'completions/min_terminated_length': 25.0, 'completions/max_terminated_length': 61.0, 'rewards/env_reward/mean': 0.75, 'rewards/env_reward/std': 0.4472, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.75, 'reward_std': 0.4629, 'frac_reward_zero_std': 0.0, 'completion_length': 42.0, 'kl': 0.182, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 1.0}\n",
+ "15:38:32 | INFO | server.services.curriculum | Episode 5: task=53 difficulty=beginner achieved=True tier_rate=1.00\n",
+ "15:38:32 | INFO | server.services.curriculum | Loaded 25 intermediate tasks total\n",
+ "15:38:32 | INFO | server.services.curriculum | PROMOTED from beginner to intermediate (rate=1.00, FAST-TRACK)\n",
+ "15:38:32 | INFO | server.services.curriculum | Episode 6: task=8 difficulty=beginner achieved=True tier_rate=0.00\n",
+ "15:38:35 | INFO | grpo | [optuna-trial-2] step 3/10 (+47.3s)\n",
+ "15:38:35 | INFO | grpo | [CurriculumPromotionCallback] tier promoted beginner -> intermediate at step 3\n",
+ "15:38:35 | INFO | grpo | [optuna-trial-2] log step=3 {'loss': 0.2396, 'grad_norm': 0.3564, 'learning_rate': 0.0, 'completion_length': 48.1875, 'kl': 0.0953, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 10195.0, 'completions/mean_length': 52.1875, 'completions/min_length': 43.0, 'completions/max_length': 69.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 52.1875, 'completions/min_terminated_length': 43.0, 'completions/max_terminated_length': 69.0, 'rewards/env_reward/mean': 0.9375, 'rewards/env_reward/std': 0.25, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.9375, 'reward_std': 0.1768, 'frac_reward_zero_std': 0.5, 'epoch': 1.6667}\n",
+ "15:38:38 | INFO | grpo | [optuna-trial-2] step 4/10 (+3.3s)\n",
+ "15:38:38 | INFO | grpo | [optuna-trial-2] log step=4 {'loss': -0.2103, 'grad_norm': 0.1872, 'learning_rate': 0.0, 'completion_length': 50.25, 'kl': 0.117, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 2.0}\n",
+ "15:40:06 | INFO | server.services.curriculum | Episode 7: task=11 difficulty=intermediate achieved=True tier_rate=1.00\n",
+ "15:40:06 | INFO | server.services.curriculum | Episode 8: task=84 difficulty=intermediate achieved=True tier_rate=1.00\n",
+ "15:40:12 | INFO | grpo | [optuna-trial-2] step 5/10 (+93.9s)\n",
+ "15:40:12 | INFO | grpo | [optuna-trial-2] log step=5 {'loss': 0.0727, 'grad_norm': 0.2555, 'learning_rate': 0.0, 'num_tokens': 14229.0, 'completions/mean_length': 68.125, 'completions/min_length': 41.0, 'completions/max_length': 102.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 68.125, 'completions/min_terminated_length': 41.0, 'completions/max_terminated_length': 102.0, 'rewards/env_reward/mean': 0.875, 'rewards/env_reward/std': 0.3416, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.875, 'reward_std': 0.3536, 'frac_reward_zero_std': 0.0, 'completion_length': 68.125, 'kl': 0.2631, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 2.6667}\n",
+ "15:42:00 | INFO | server.services.curriculum | Loaded 25 advanced tasks total\n",
+ "15:42:00 | INFO | server.services.curriculum | PROMOTED from intermediate to advanced (rate=1.00, FAST-TRACK)\n",
+ "15:42:00 | INFO | server.services.curriculum | Episode 9: task=84 difficulty=intermediate achieved=True tier_rate=0.00\n",
+ "15:42:00 | INFO | server.services.curriculum | Episode 10: task=11 difficulty=intermediate achieved=True tier_rate=1.00\n",
+ "15:42:04 | INFO | grpo | [optuna-trial-2] step 6/10 (+111.3s)\n",
+ "15:42:04 | INFO | grpo | [CurriculumPromotionCallback] tier promoted intermediate -> advanced at step 6\n",
+ "15:42:04 | INFO | grpo | [optuna-trial-2] log step=6 {'loss': 0.289, 'grad_norm': 0.7695, 'learning_rate': 0.0, 'num_tokens': 18160.0, 'completions/mean_length': 61.6875, 'completions/min_length': 36.0, 'completions/max_length': 81.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 61.6875, 'completions/min_terminated_length': 36.0, 'completions/max_terminated_length': 81.0, 'rewards/env_reward/mean': 0.6875, 'rewards/env_reward/std': 0.4787, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9984, 'rewards/length_reward/std': 0.0062, 'reward': 2.6859, 'reward_std': 0.4924, 'frac_reward_zero_std': 0.0, 'completion_length': 57.875, 'kl': 0.3904, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 3.0}\n",
+ "15:45:46 | INFO | server.services.curriculum | Episode 11: task=95 difficulty=advanced achieved=True tier_rate=1.00\n",
+ "15:45:46 | INFO | server.services.curriculum | Episode 12: task=96 difficulty=advanced achieved=False tier_rate=0.61\n",
+ "15:45:46 | INFO | server.services.curriculum | Episode 13: task=97 difficulty=advanced achieved=False tier_rate=0.42\n",
+ "15:45:50 | INFO | grpo | [optuna-trial-2] step 7/10 (+225.9s)\n",
+ "15:45:50 | INFO | grpo | [optuna-trial-2] log step=7 {'loss': -0.1, 'grad_norm': 0.3597, 'learning_rate': 0.0, 'completion_length': 73.25, 'kl': 0.2593, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 23010.0, 'completions/mean_length': 77.625, 'completions/min_length': 41.0, 'completions/max_length': 116.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 77.625, 'completions/min_terminated_length': 41.0, 'completions/max_terminated_length': 116.0, 'rewards/env_reward/mean': 0.6021, 'rewards/env_reward/std': 0.2387, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9379, 'rewards/length_reward/std': 0.0828, 'reward': 2.54, 'reward_std': 0.2429, 'frac_reward_zero_std': 0.0, 'epoch': 3.6667}\n",
+ "15:45:54 | INFO | grpo | [optuna-trial-2] step 8/10 (+4.0s)\n",
+ "15:45:54 | INFO | grpo | [optuna-trial-2] log step=8 {'loss': 0.2722, 'grad_norm': 0.2342, 'learning_rate': 0.0, 'completion_length': 74.25, 'kl': 0.2081, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.0}\n",
+ "15:49:30 | INFO | server.services.curriculum | Episode 14: task=96 difficulty=advanced achieved=False tier_rate=0.31\n",
+ "15:49:30 | INFO | server.services.curriculum | Episode 15: task=95 difficulty=advanced achieved=True tier_rate=0.47\n",
+ "15:49:30 | INFO | server.services.curriculum | Episode 16: task=97 difficulty=advanced achieved=False tier_rate=0.37\n",
+ "15:49:38 | INFO | grpo | [optuna-trial-2] step 9/10 (+224.5s)\n",
+ "15:49:38 | INFO | grpo | [optuna-trial-2] log step=9 {'loss': 0.0158, 'grad_norm': 0.2369, 'learning_rate': 0.0, 'num_tokens': 27900.0, 'completions/mean_length': 80.125, 'completions/min_length': 36.0, 'completions/max_length': 139.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 80.125, 'completions/min_terminated_length': 36.0, 'completions/max_terminated_length': 139.0, 'rewards/env_reward/mean': 0.5644, 'rewards/env_reward/std': 0.2448, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9235, 'rewards/length_reward/std': 0.0927, 'reward': 2.4879, 'reward_std': 0.2535, 'frac_reward_zero_std': 0.0, 'completion_length': 80.125, 'kl': 0.2495, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.6667}\n",
+ "15:53:15 | INFO | server.services.curriculum | Task 95 GRADUATED (rate=1.00) — scheduling spaced repetition\n",
+ "15:53:15 | INFO | server.services.curriculum | Episode 17: task=95 difficulty=advanced achieved=True tier_rate=0.50\n",
+ "15:53:15 | INFO | server.services.curriculum | Episode 18: task=97 difficulty=advanced achieved=False tier_rate=0.40\n",
+ "15:53:15 | INFO | server.services.curriculum | Episode 19: task=96 difficulty=advanced achieved=False tier_rate=0.33\n",
+ "15:53:19 | INFO | grpo | [optuna-trial-2] step 10/10 (+221.0s)\n",
+ "15:53:19 | INFO | grpo | [optuna-trial-2] log step=10 {'loss': -0.3654, 'grad_norm': 0.265, 'learning_rate': 0.0, 'num_tokens': 32841.0, 'completions/mean_length': 83.3125, 'completions/min_length': 32.0, 'completions/max_length': 159.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 83.3125, 'completions/min_terminated_length': 32.0, 'completions/max_terminated_length': 159.0, 'rewards/env_reward/mean': 0.5631, 'rewards/env_reward/std': 0.3121, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9087, 'rewards/length_reward/std': 0.0968, 'reward': 2.4718, 'reward_std': 0.3662, 'frac_reward_zero_std': 0.0, 'completion_length': 65.75, 'kl': 0.3361, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 5.0}\n",
+ "15:53:19 | INFO | grpo | [optuna-trial-2] log step=10 {'train_runtime': 992.7264, 'train_samples_per_second': 0.161, 'train_steps_per_second': 0.01, 'total_flos': 0.0, 'train_loss': -0.0104, 'epoch': 5.0}\n",
+ "15:53:19 | INFO | grpo | [optuna-trial-2] train_end | global_step=10 elapsed=992.7s\n",
+ "15:53:20 | INFO | grpo | [trial 2] train() finished in 998.5s\n",
+ "15:53:20 | INFO | grpo | [trial 2] running single-step eval on 12 tasks...\n",
+ "15:54:35 | INFO | grpo | [trial 2] eval finished in 75.0s\n",
+ "15:54:35 | INFO | grpo | [trial 2] DONE | env_reward_mean=0.4687 success=0.250 tier=advanced graduated=1\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[I 2026-04-25 15:54:38,863] Trial 2 pruned. \n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "15:54:38 | INFO | grpo | [trial 3] starting | cfg={'lr': 1.5958573588141284e-05, 'beta': 0.0020584494295802446, 'temp': 0.9909729556485982}\n",
+ "15:54:38 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "15:54:38 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "15:54:38 | INFO | grpo | [trial 3] loading SFT policy (4-bit Qwen2.5-Coder-3B)...\n",
+ "==((====))== Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 4.57.6.\n",
+ " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n",
+ "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.35. FA2 = False]\n",
+ " \"-____-\" Free license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
+ "15:54:57 | INFO | grpo | [trial 3] policy loaded in 18.2s\n",
+ "15:54:57 | INFO | grpo | CurriculumTierSampler: num_samples=192, tier sizes={'warmup': 2, 'beginner': 2, 'intermediate': 2, 'advanced': 3, 'expert': 3}\n",
+ "15:54:57 | INFO | grpo | [trial 3] starting train() for 10 steps...\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
+ " \\\\ /| Num examples = 12 | Num Epochs = 5 | Total steps = 10\n",
+ "O^O/ \\_/ \\ Batch size per device = 2 | Gradient accumulation steps = 8\n",
+ "\\ / Data Parallel GPUs = 1 | Total batch size (2 x 8 x 1) = 16\n",
+ " \"-____-\" Trainable parameters = 7,372,800 of 3,093,311,488 (0.24% trained)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "15:55:01 | INFO | grpo | [optuna-trial-3] train_begin | max_steps=10\n",
+ "Unsloth: Will smartly offload gradients to save VRAM!\n",
+ "15:55:26 | INFO | server.services.curriculum | Episode 1: task=33 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "15:55:26 | INFO | server.services.curriculum | Episode 2: task=37 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "15:55:35 | INFO | grpo | [optuna-trial-3] step 1/10 (+34.0s)\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ],
+ "text/html": [
+ "\n",
+ " \n",
+ " \n",
+ "
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+ " [10/10 15:06, Epoch 5/5]\n",
+ "
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+ " \n",
+ " \n",
+ " | Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ "
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+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "15:55:35 | INFO | grpo | [optuna-trial-3] log step=1 {'loss': -0.062, 'grad_norm': 0.3466, 'learning_rate': 0.0, 'num_tokens': 3349.0, 'completions/mean_length': 43.3125, 'completions/min_length': 24.0, 'completions/max_length': 61.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 43.3125, 'completions/min_terminated_length': 24.0, 'completions/max_terminated_length': 61.0, 'rewards/env_reward/mean': 0.8125, 'rewards/env_reward/std': 0.4031, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.8125, 'reward_std': 0.4082, 'frac_reward_zero_std': 0.0, 'completion_length': 43.3125, 'kl': 0.1133, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.6667}\n",
+ "15:56:04 | INFO | server.services.curriculum | Loaded 25 beginner tasks total\n",
+ "15:56:04 | INFO | server.services.curriculum | PROMOTED from warmup to beginner (rate=1.00, FAST-TRACK)\n",
+ "15:56:04 | INFO | server.services.curriculum | Episode 3: task=37 difficulty=warmup achieved=True tier_rate=0.00\n",
+ "15:56:04 | INFO | server.services.curriculum | Episode 4: task=33 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "15:56:07 | INFO | grpo | [optuna-trial-3] step 2/10 (+32.6s)\n",
+ "15:56:07 | INFO | grpo | [CurriculumPromotionCallback] tier promoted warmup -> beginner at step 2\n",
+ "15:56:07 | INFO | grpo | [optuna-trial-3] log step=2 {'loss': -0.0776, 'grad_norm': 0.4329, 'learning_rate': 0.0, 'num_tokens': 6727.0, 'completions/mean_length': 45.125, 'completions/min_length': 25.0, 'completions/max_length': 70.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 45.125, 'completions/min_terminated_length': 25.0, 'completions/max_terminated_length': 70.0, 'rewards/env_reward/mean': 0.75, 'rewards/env_reward/std': 0.4472, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.75, 'reward_std': 0.4629, 'frac_reward_zero_std': 0.0, 'completion_length': 49.75, 'kl': 0.1218, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 1.0}\n",
+ "15:56:38 | INFO | server.services.curriculum | Episode 5: task=53 difficulty=beginner achieved=True tier_rate=1.00\n",
+ "15:56:38 | INFO | server.services.curriculum | Loaded 25 intermediate tasks total\n",
+ "15:56:38 | INFO | server.services.curriculum | PROMOTED from beginner to intermediate (rate=1.00, FAST-TRACK)\n",
+ "15:56:38 | INFO | server.services.curriculum | Episode 6: task=8 difficulty=beginner achieved=True tier_rate=0.00\n",
+ "15:56:41 | INFO | grpo | [optuna-trial-3] step 3/10 (+34.4s)\n",
+ "15:56:41 | INFO | grpo | [CurriculumPromotionCallback] tier promoted beginner -> intermediate at step 3\n",
+ "15:56:41 | INFO | grpo | [optuna-trial-3] log step=3 {'loss': -0.0391, 'grad_norm': 0.2092, 'learning_rate': 0.0, 'completion_length': 50.875, 'kl': 0.0939, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 10356.0, 'completions/mean_length': 55.8125, 'completions/min_length': 35.0, 'completions/max_length': 80.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 55.8125, 'completions/min_terminated_length': 35.0, 'completions/max_terminated_length': 80.0, 'rewards/env_reward/mean': 1.0, 'rewards/env_reward/std': 0.0, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 3.0, 'reward_std': 0.0, 'frac_reward_zero_std': 1.0, 'epoch': 1.6667}\n",
+ "15:56:45 | INFO | grpo | [optuna-trial-3] step 4/10 (+3.0s)\n",
+ "15:56:45 | INFO | grpo | [optuna-trial-3] log step=4 {'loss': 0.0002, 'grad_norm': 0.0008, 'learning_rate': 0.0, 'completion_length': 50.375, 'kl': 0.0983, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 2.0}\n",
+ "15:58:11 | INFO | server.services.curriculum | Episode 7: task=11 difficulty=intermediate achieved=True tier_rate=1.00\n",
+ "15:58:11 | INFO | server.services.curriculum | Episode 8: task=84 difficulty=intermediate achieved=True tier_rate=1.00\n",
+ "15:58:18 | INFO | grpo | [optuna-trial-3] step 5/10 (+93.1s)\n",
+ "15:58:18 | INFO | grpo | [optuna-trial-3] log step=5 {'loss': 0.0104, 'grad_norm': 0.247, 'learning_rate': 0.0, 'num_tokens': 14590.0, 'completions/mean_length': 80.625, 'completions/min_length': 49.0, 'completions/max_length': 114.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 80.625, 'completions/min_terminated_length': 49.0, 'completions/max_terminated_length': 114.0, 'rewards/env_reward/mean': 0.75, 'rewards/env_reward/std': 0.4472, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9989, 'rewards/length_reward/std': 0.0045, 'reward': 2.7489, 'reward_std': 0.465, 'frac_reward_zero_std': 0.0, 'completion_length': 80.625, 'kl': 0.1865, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 2.6667}\n",
+ "15:59:38 | INFO | server.services.curriculum | Loaded 25 advanced tasks total\n",
+ "15:59:38 | INFO | server.services.curriculum | PROMOTED from intermediate to advanced (rate=1.00, FAST-TRACK)\n",
+ "15:59:38 | INFO | server.services.curriculum | Episode 9: task=84 difficulty=intermediate achieved=True tier_rate=0.00\n",
+ "15:59:38 | INFO | server.services.curriculum | Episode 10: task=11 difficulty=intermediate achieved=True tier_rate=1.00\n",
+ "15:59:41 | INFO | grpo | [optuna-trial-3] step 6/10 (+83.4s)\n",
+ "15:59:41 | INFO | grpo | [CurriculumPromotionCallback] tier promoted intermediate -> advanced at step 6\n",
+ "15:59:41 | INFO | grpo | [optuna-trial-3] log step=6 {'loss': 0.0046, 'grad_norm': 0.3215, 'learning_rate': 0.0, 'num_tokens': 18686.0, 'completions/mean_length': 72.0, 'completions/min_length': 50.0, 'completions/max_length': 121.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 72.0, 'completions/min_terminated_length': 50.0, 'completions/max_terminated_length': 121.0, 'rewards/env_reward/mean': 0.875, 'rewards/env_reward/std': 0.3416, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9961, 'rewards/length_reward/std': 0.0156, 'reward': 2.8711, 'reward_std': 0.3523, 'frac_reward_zero_std': 0.0, 'completion_length': 72.625, 'kl': 0.2362, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 3.0}\n",
+ "16:03:16 | INFO | server.services.curriculum | Episode 11: task=97 difficulty=advanced achieved=False tier_rate=0.46\n",
+ "16:03:16 | INFO | server.services.curriculum | Episode 12: task=96 difficulty=advanced achieved=False tier_rate=0.28\n",
+ "16:03:16 | INFO | server.services.curriculum | Episode 13: task=95 difficulty=advanced achieved=True tier_rate=0.51\n",
+ "16:03:20 | INFO | grpo | [optuna-trial-3] step 7/10 (+218.4s)\n",
+ "16:03:20 | INFO | grpo | [optuna-trial-3] log step=7 {'loss': 0.0912, 'grad_norm': 0.2479, 'learning_rate': 0.0, 'completion_length': 82.3125, 'kl': 0.1815, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 23688.0, 'completions/mean_length': 87.5, 'completions/min_length': 32.0, 'completions/max_length': 132.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 87.5, 'completions/min_terminated_length': 32.0, 'completions/max_terminated_length': 132.0, 'rewards/env_reward/mean': 0.601, 'rewards/env_reward/std': 0.2852, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9089, 'rewards/length_reward/std': 0.0832, 'reward': 2.51, 'reward_std': 0.3004, 'frac_reward_zero_std': 0.0, 'epoch': 3.6667}\n",
+ "16:03:24 | INFO | grpo | [optuna-trial-3] step 8/10 (+4.0s)\n",
+ "16:03:24 | INFO | grpo | [optuna-trial-3] log step=8 {'loss': 0.0237, 'grad_norm': 0.1846, 'learning_rate': 0.0, 'completion_length': 81.75, 'kl': 0.2095, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.0}\n",
+ "16:06:54 | INFO | server.services.curriculum | Episode 14: task=97 difficulty=advanced achieved=False tier_rate=0.37\n",
+ "16:06:54 | INFO | server.services.curriculum | Episode 15: task=95 difficulty=advanced achieved=True tier_rate=0.52\n",
+ "16:06:54 | INFO | server.services.curriculum | Episode 16: task=96 difficulty=advanced achieved=False tier_rate=0.41\n",
+ "16:07:03 | INFO | grpo | [optuna-trial-3] step 9/10 (+219.1s)\n",
+ "16:07:03 | INFO | grpo | [optuna-trial-3] log step=9 {'loss': 0.0859, 'grad_norm': 0.1679, 'learning_rate': 0.0, 'num_tokens': 28764.0, 'completions/mean_length': 91.75, 'completions/min_length': 32.0, 'completions/max_length': 141.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 91.75, 'completions/min_terminated_length': 32.0, 'completions/max_terminated_length': 141.0, 'rewards/env_reward/mean': 0.6329, 'rewards/env_reward/std': 0.268, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.918, 'rewards/length_reward/std': 0.0851, 'reward': 2.5509, 'reward_std': 0.3024, 'frac_reward_zero_std': 0.0, 'completion_length': 91.75, 'kl': 0.1948, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.6667}\n",
+ "16:10:37 | INFO | server.services.curriculum | Episode 17: task=96 difficulty=advanced achieved=False tier_rate=0.32\n",
+ "16:10:37 | INFO | server.services.curriculum | Episode 18: task=97 difficulty=advanced achieved=False tier_rate=0.26\n",
+ "16:10:37 | INFO | server.services.curriculum | Task 95 GRADUATED (rate=1.00) — scheduling spaced repetition\n",
+ "16:10:37 | INFO | server.services.curriculum | Episode 19: task=95 difficulty=advanced achieved=True tier_rate=0.40\n",
+ "16:10:41 | INFO | grpo | [optuna-trial-3] step 10/10 (+218.2s)\n",
+ "16:10:41 | INFO | grpo | [optuna-trial-3] log step=10 {'loss': -0.0137, 'grad_norm': 0.2202, 'learning_rate': 0.0, 'num_tokens': 33936.0, 'completions/mean_length': 98.125, 'completions/min_length': 57.0, 'completions/max_length': 143.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 98.125, 'completions/min_terminated_length': 57.0, 'completions/max_terminated_length': 143.0, 'rewards/env_reward/mean': 0.7233, 'rewards/env_reward/std': 0.2071, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9179, 'rewards/length_reward/std': 0.0851, 'reward': 2.6412, 'reward_std': 0.2571, 'frac_reward_zero_std': 0.0, 'completion_length': 99.375, 'kl': 0.1591, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 5.0}\n",
+ "16:10:41 | INFO | grpo | [optuna-trial-3] log step=10 {'train_runtime': 940.2587, 'train_samples_per_second': 0.17, 'train_steps_per_second': 0.011, 'total_flos': 0.0, 'train_loss': 0.0024, 'epoch': 5.0}\n",
+ "16:10:41 | INFO | grpo | [optuna-trial-3] train_end | global_step=10 elapsed=940.3s\n",
+ "16:10:42 | INFO | grpo | [trial 3] train() finished in 945.2s\n",
+ "16:10:42 | INFO | grpo | [trial 3] running single-step eval on 12 tasks...\n",
+ "16:12:01 | INFO | grpo | [trial 3] eval finished in 78.8s\n",
+ "16:12:01 | INFO | grpo | [trial 3] DONE | env_reward_mean=0.5520 success=0.333 tier=advanced graduated=1\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "[I 2026-04-25 16:12:04,437] Trial 3 finished with value: 0.552 and parameters: {'learning_rate': 1.5958573588141284e-05, 'beta': 0.0020584494295802446, 'temperature': 0.9909729556485982}. Best is trial 3 with value: 0.552.\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Best trial env_reward_mean = 0.5520\n",
+ "Best params: {'learning_rate': 1.5958573588141284e-05, 'beta': 0.0020584494295802446, 'temperature': 0.9909729556485982}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Reclaim VRAM before starting trials. If the user ran the final-run cell\n",
+ "# first (e.g. because optuna_best.json didn't exist on a prior session),\n",
+ "# `final_model` / `final_trainer` / `_probe_model` may still be resident\n",
+ "# on the GPU. Each trial loads a fresh 4-bit Qwen (~6 GB) via\n",
+ "# load_policy, and on a 14.5 GB T4 the second model won't fit alongside\n",
+ "# leftovers, so BitsAndBytes falls back to CPU offload and errors out\n",
+ "# (\"Some modules are dispatched on the CPU or the disk\").\n",
+ "for _name in (\"final_trainer\", \"final_model\", \"final_tok\", \"_probe_model\", \"_probe_tok\", \"_baseline_model\", \"_baseline_tok\"):\n",
+ " if _name in globals():\n",
+ " try:\n",
+ " _obj = globals().pop(_name)\n",
+ " del _obj\n",
+ " except Exception:\n",
+ " pass\n",
+ "gc.collect()\n",
+ "try:\n",
+ " torch.cuda.empty_cache()\n",
+ "except Exception:\n",
+ " pass\n",
+ "\n",
+ "STUDY_DB = OUT_DIR / \"optuna.db\"\n",
+ "STUDY_NAME = \"aws-rl-grpo-search\"\n",
+ "\n",
+ "study = optuna.create_study(\n",
+ " direction=\"maximize\",\n",
+ " sampler=optuna.samplers.TPESampler(seed=TRAIN.seed),\n",
+ " pruner=optuna.pruners.MedianPruner(n_startup_trials=2, n_warmup_steps=10),\n",
+ " study_name=STUDY_NAME,\n",
+ " storage=f\"sqlite:///{STUDY_DB}\",\n",
+ " load_if_exists=True,\n",
+ ")\n",
+ "\n",
+ "completed = sum(1 for t in study.trials if t.state.name == \"COMPLETE\")\n",
+ "remaining = max(0, PIPE.n_trials - completed)\n",
+ "print(f\"Optuna study '{STUDY_NAME}': {completed} completed, {remaining} remaining.\")\n",
+ "\n",
+ "if remaining > 0:\n",
+ " study.optimize(trial_objective, n_trials=remaining)\n",
+ "\n",
+ "best_cfg = replace(TRAIN, **{\n",
+ " k: v for k, v in study.best_params.items() if k in TrainingConfig.__dataclass_fields__\n",
+ "})\n",
+ "(OUT_DIR / \"optuna_best.json\").write_text(json.dumps({\n",
+ " \"best_value\": study.best_value,\n",
+ " \"best_params\": study.best_params,\n",
+ " \"resolved_config\": asdict(best_cfg),\n",
+ "}, indent=2))\n",
+ "\n",
+ "print(f\"Best trial env_reward_mean = {study.best_value:.4f}\")\n",
+ "print(f\"Best params: {study.best_params}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "278540e2",
+ "metadata": {
+ "id": "278540e2",
+ "outputId": "a99f73e2-0907-4a46-86fc-ccf61a361cb8",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/tmp/ipykernel_1159/2728918386.py:17: ExperimentalWarning: optuna.visualization.matplotlib._optimization_history.plot_optimization_history is experimental (supported from v2.2.0). The interface can change in the future.\n",
+ " ax = fn(study)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "16:12:05 | INFO | grpo | Saved /content/out/graphs/00_optuna_history.png\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/tmp/ipykernel_1159/2728918386.py:17: ExperimentalWarning: optuna.visualization.matplotlib._parallel_coordinate.plot_parallel_coordinate is experimental (supported from v2.2.0). The interface can change in the future.\n",
+ " ax = fn(study)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "16:12:05 | INFO | grpo | Saved /content/out/graphs/00_optuna_parallel.png\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/tmp/ipykernel_1159/2728918386.py:17: ExperimentalWarning: optuna.visualization.matplotlib._param_importances.plot_param_importances is experimental (supported from v2.2.0). The interface can change in the future.\n",
+ " ax = fn(study)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "16:12:06 | INFO | grpo | Saved /content/out/graphs/00_optuna_importances.png\n"
+ ]
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from optuna.visualization.matplotlib import (\n",
+ " plot_optimization_history,\n",
+ " plot_parallel_coordinate,\n",
+ " plot_param_importances,\n",
+ ")\n",
+ "\n",
+ "GRAPHS_DIR = OUT_DIR / \"graphs\"\n",
+ "GRAPHS_DIR.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "for name, fn in [\n",
+ " (\"history\", plot_optimization_history),\n",
+ " (\"parallel\", plot_parallel_coordinate),\n",
+ " (\"importances\", plot_param_importances),\n",
+ "]:\n",
+ " try:\n",
+ " ax = fn(study)\n",
+ " fig = ax.figure\n",
+ " out_png = GRAPHS_DIR / f\"00_optuna_{name}.png\"\n",
+ " fig.savefig(out_png, dpi=150, bbox_inches=\"tight\")\n",
+ " plt.close(fig)\n",
+ " log.info(\"Saved %s\", out_png)\n",
+ " except Exception as e:\n",
+ " log.warning(\"Optuna %s plot skipped: %s\", name, e)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c8528094",
+ "metadata": {
+ "id": "c8528094"
+ },
+ "source": [
+ "## 14 · Final GRPO run — best hparams, full data, checkpointed\n",
+ "\n",
+ "Uses the Optuna winner on the full 1-epoch training set with periodic checkpoints (`save_steps=25`, `save_total_limit=3`). `CheckpointManager.latest()` auto-detects a prior checkpoint so re-running this cell after a disconnect resumes safely.\n",
+ "\n",
+ "Wandb run `final-grpo` gets a stable run id so resumed training stitches into the same panel instead of creating a fresh chart."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "74e46eed",
+ "metadata": {
+ "id": "74e46eed",
+ "outputId": "d0bb5912-5962-42d9-eba6-64bf73663c86",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "16:12:28 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "16:12:28 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "16:12:28 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "16:12:28 | INFO | server.services.curriculum | Loaded 25 beginner tasks total\n",
+ "16:12:28 | INFO | server.services.curriculum | Loaded 25 intermediate tasks total\n",
+ "16:12:28 | INFO | server.services.curriculum | Loaded 25 advanced tasks total\n",
+ "16:12:28 | INFO | server.services.curriculum | Loaded 9 supplementary expert tasks from drift.yaml\n",
+ "16:12:28 | INFO | server.services.curriculum | Loaded 33 expert tasks total\n",
+ "16:12:28 | INFO | grpo | Full curriculum dataset: 133 rows across tiers ['advanced', 'beginner', 'expert', 'intermediate', 'warmup']\n",
+ "==((====))== Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 4.57.6.\n",
+ " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n",
+ "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.35. FA2 = False]\n",
+ " \"-____-\" Free license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
+ "16:12:48 | INFO | grpo | CurriculumTierSampler: num_samples=672, tier sizes={'warmup': 25, 'beginner': 25, 'intermediate': 25, 'advanced': 25, 'expert': 33}\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
+ " \\\\ /| Num examples = 133 | Num Epochs = 6 | Total steps = 35\n",
+ "O^O/ \\_/ \\ Batch size per device = 2 | Gradient accumulation steps = 8\n",
+ "\\ / Data Parallel GPUs = 1 | Total batch size (2 x 8 x 1) = 16\n",
+ " \"-____-\" Trainable parameters = 7,372,800 of 3,093,311,488 (0.24% trained)\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "16:12:52 | INFO | grpo | [final-grpo] train_begin | max_steps=35\n",
+ "Unsloth: Will smartly offload gradients to save VRAM!\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 1: task=37 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 2: task=33 difficulty=warmup achieved=False tier_rate=0.46\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 3: task=30 difficulty=warmup achieved=True tier_rate=0.67\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 4: task=40 difficulty=warmup achieved=True tier_rate=0.77\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 5: task=39 difficulty=warmup achieved=False tier_rate=0.56\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 6: task=27 difficulty=warmup achieved=False tier_rate=0.43\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 7: task=5 difficulty=warmup achieved=True tier_rate=0.55\n",
+ "16:13:18 | INFO | server.services.curriculum | Loaded 25 beginner tasks total\n",
+ "16:13:18 | INFO | server.services.curriculum | PROMOTED from warmup to beginner (rate=0.65)\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 8: task=31 difficulty=warmup achieved=True tier_rate=0.00\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 9: task=36 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 10: task=42 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:18 | INFO | server.services.curriculum | Loaded 25 intermediate tasks total\n",
+ "16:13:18 | INFO | server.services.curriculum | PROMOTED from beginner to intermediate (rate=1.00, FAST-TRACK)\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 11: task=32 difficulty=warmup achieved=True tier_rate=0.00\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 12: task=38 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 13: task=1 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:18 | INFO | server.services.curriculum | Loaded 25 advanced tasks total\n",
+ "16:13:18 | INFO | server.services.curriculum | PROMOTED from intermediate to advanced (rate=1.00, FAST-TRACK)\n",
+ "16:13:18 | INFO | server.services.curriculum | Episode 14: task=35 difficulty=warmup achieved=True tier_rate=0.00\n",
+ "16:13:19 | INFO | server.services.curriculum | Episode 15: task=43 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:19 | INFO | server.services.curriculum | Episode 16: task=34 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:25 | INFO | grpo | [final-grpo] step 1/35 (+33.1s)\n"
+ ]
+ },
+ {
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+ " 0.000000 | \n",
+ " 81.937500 | \n",
+ " 24.000000 | \n",
+ " 168.000000 | \n",
+ " 0.139455 | \n",
+ " 0.217375 | \n",
+ " 0.252687 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 0.920647 | \n",
+ " 0.142823 | \n",
+ "
\n",
+ " \n",
+ " | 30 | \n",
+ " 0.637700 | \n",
+ " 2.181848 | \n",
+ " 0.346811 | \n",
+ " 73.187500 | \n",
+ " 38.000000 | \n",
+ " 151.000000 | \n",
+ " 0.000000 | \n",
+ " 73.187500 | \n",
+ " 38.000000 | \n",
+ " 151.000000 | \n",
+ " 0.171576 | \n",
+ " 0.231625 | \n",
+ " 0.326569 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 0.950223 | \n",
+ " 0.086972 | \n",
+ "
\n",
+ " \n",
+ " | 31 | \n",
+ " -0.112300 | \n",
+ " 2.351836 | \n",
+ " 0.442533 | \n",
+ " 88.812500 | \n",
+ " 47.000000 | \n",
+ " 147.000000 | \n",
+ " 0.000000 | \n",
+ " 88.812500 | \n",
+ " 47.000000 | \n",
+ " 147.000000 | \n",
+ " 0.162624 | \n",
+ " 0.394917 | \n",
+ " 0.418818 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 0.956920 | \n",
+ " 0.104350 | \n",
+ "
\n",
+ " \n",
+ " | 32 | \n",
+ " 0.163400 | \n",
+ " 2.198289 | \n",
+ " 0.489258 | \n",
+ " 83.937500 | \n",
+ " 29.000000 | \n",
+ " 182.000000 | \n",
+ " 0.000000 | \n",
+ " 83.937500 | \n",
+ " 29.000000 | \n",
+ " 182.000000 | \n",
+ " 0.142202 | \n",
+ " 0.295833 | \n",
+ " 0.389247 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 0.902455 | \n",
+ " 0.138221 | \n",
+ "
\n",
+ " \n",
+ " | 33 | \n",
+ " 0.187300 | \n",
+ " 2.262478 | \n",
+ " 0.567792 | \n",
+ " 101.812500 | \n",
+ " 32.000000 | \n",
+ " 194.000000 | \n",
+ " 0.000000 | \n",
+ " 101.812500 | \n",
+ " 32.000000 | \n",
+ " 194.000000 | \n",
+ " 0.158527 | \n",
+ " 0.389375 | \n",
+ " 0.448248 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 0.873103 | \n",
+ " 0.162099 | \n",
+ "
\n",
+ " \n",
+ " | 34 | \n",
+ " -0.209300 | \n",
+ " 2.182246 | \n",
+ " 0.270923 | \n",
+ " 80.437500 | \n",
+ " 26.000000 | \n",
+ " 129.000000 | \n",
+ " 0.000000 | \n",
+ " 80.437500 | \n",
+ " 26.000000 | \n",
+ " 129.000000 | \n",
+ " 0.134107 | \n",
+ " 0.236375 | \n",
+ " 0.231756 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 0.945871 | \n",
+ " 0.073594 | \n",
+ "
\n",
+ " \n",
+ " | 35 | \n",
+ " -0.061500 | \n",
+ " 2.124198 | \n",
+ " 0.254833 | \n",
+ " 83.875000 | \n",
+ " 30.000000 | \n",
+ " 249.000000 | \n",
+ " 0.000000 | \n",
+ " 83.875000 | \n",
+ " 30.000000 | \n",
+ " 249.000000 | \n",
+ " 0.125973 | \n",
+ " 0.192167 | \n",
+ " 0.186760 | \n",
+ " 1.000000 | \n",
+ " 0.000000 | \n",
+ " 0.932031 | \n",
+ " 0.122966 | \n",
+ "
\n",
+ " \n",
+ "
"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "16:13:25 | INFO | grpo | [final-grpo] log step=1 {'loss': 0.0313, 'grad_norm': 0.5735, 'learning_rate': 0.0, 'num_tokens': 3235.0, 'completions/mean_length': 37.625, 'completions/min_length': 21.0, 'completions/max_length': 69.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 37.625, 'completions/min_terminated_length': 21.0, 'completions/max_terminated_length': 69.0, 'rewards/env_reward/mean': 0.8125, 'rewards/env_reward/std': 0.4031, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.8125, 'reward_std': 0.2588, 'frac_reward_zero_std': 0.5, 'completion_length': 37.625, 'kl': 0.16, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.1905}\n",
+ "16:13:51 | INFO | server.services.curriculum | Loaded 9 supplementary expert tasks from drift.yaml\n",
+ "16:13:51 | INFO | server.services.curriculum | Loaded 33 expert tasks total\n",
+ "16:13:51 | INFO | server.services.curriculum | PROMOTED from advanced to expert (rate=1.00, FAST-TRACK)\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 17: task=44 difficulty=warmup achieved=True tier_rate=0.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 18: task=29 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 19: task=34 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 20: task=38 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 21: task=35 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 22: task=3 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 23: task=37 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 24: task=43 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 25: task=36 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 26: task=0 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 27: task=4 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 28: task=28 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 29: task=41 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 30: task=1 difficulty=warmup achieved=True tier_rate=1.00\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 31: task=33 difficulty=warmup achieved=False tier_rate=0.83\n",
+ "16:13:51 | INFO | server.services.curriculum | Episode 32: task=5 difficulty=warmup achieved=True tier_rate=0.86\n",
+ "16:13:57 | INFO | grpo | [final-grpo] step 2/35 (+31.9s)\n",
+ "16:13:57 | INFO | grpo | [CurriculumPromotionCallback] tier promoted advanced -> expert at step 2\n",
+ "16:13:57 | INFO | grpo | [final-grpo] log step=2 {'loss': 0.0012, 'grad_norm': 0.2202, 'learning_rate': 0.0, 'num_tokens': 6578.0, 'completions/mean_length': 44.8125, 'completions/min_length': 17.0, 'completions/max_length': 75.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 44.8125, 'completions/min_terminated_length': 17.0, 'completions/max_terminated_length': 75.0, 'rewards/env_reward/mean': 0.9375, 'rewards/env_reward/std': 0.25, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 1.0, 'rewards/length_reward/std': 0.0, 'reward': 2.9375, 'reward_std': 0.1768, 'frac_reward_zero_std': 0.5, 'completion_length': 44.8125, 'kl': 0.1413, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.381}\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 33: task=100 difficulty=advanced achieved=False tier_rate=0.72\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 34: task=103 difficulty=advanced achieved=False tier_rate=0.61\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 35: task=97 difficulty=advanced achieved=False tier_rate=0.51\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 36: task=90 difficulty=advanced achieved=False tier_rate=0.43\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 37: task=95 difficulty=advanced achieved=False tier_rate=0.36\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 38: task=91 difficulty=advanced achieved=False tier_rate=0.31\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 39: task=101 difficulty=advanced achieved=False tier_rate=0.26\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 40: task=104 difficulty=advanced achieved=False tier_rate=0.22\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 41: task=105 difficulty=advanced achieved=False tier_rate=0.19\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 42: task=17 difficulty=advanced achieved=False tier_rate=0.16\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 43: task=107 difficulty=advanced achieved=False tier_rate=0.13\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 44: task=108 difficulty=advanced achieved=False tier_rate=0.11\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 45: task=92 difficulty=advanced achieved=False tier_rate=0.10\n",
+ "16:17:48 | INFO | server.services.curriculum | Episode 46: task=106 difficulty=advanced achieved=False tier_rate=0.08\n",
+ "16:17:58 | INFO | grpo | [final-grpo] step 3/35 (+241.3s)\n",
+ "16:17:58 | INFO | grpo | [final-grpo] log step=3 {'loss': -0.0523, 'grad_norm': 0.2405, 'learning_rate': 0.0, 'num_tokens': 11951.0, 'completions/mean_length': 109.6875, 'completions/min_length': 62.0, 'completions/max_length': 242.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 109.6875, 'completions/min_terminated_length': 62.0, 'completions/max_terminated_length': 242.0, 'rewards/env_reward/mean': 0.3119, 'rewards/env_reward/std': 0.2752, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9069, 'rewards/length_reward/std': 0.1595, 'reward': 2.2188, 'reward_std': 0.3361, 'frac_reward_zero_std': 0.0, 'completion_length': 109.6875, 'kl': 0.177, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.5714}\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 47: task=111 difficulty=expert achieved=False tier_rate=0.07\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 48: task=119 difficulty=expert achieved=True tier_rate=0.21\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 49: task=129 difficulty=expert achieved=False tier_rate=0.18\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 50: task=121 difficulty=expert achieved=False tier_rate=0.15\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 51: task=23 difficulty=expert achieved=False tier_rate=0.13\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 52: task=139 difficulty=expert achieved=False tier_rate=0.11\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 53: task=124 difficulty=expert achieved=False tier_rate=0.09\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 54: task=22 difficulty=expert achieved=False tier_rate=0.08\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 55: task=114 difficulty=expert achieved=False tier_rate=0.07\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 56: task=134 difficulty=expert achieved=False tier_rate=0.06\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 57: task=133 difficulty=expert achieved=False tier_rate=0.05\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 58: task=21 difficulty=expert achieved=True tier_rate=0.19\n",
+ "16:21:17 | INFO | server.services.curriculum | Episode 59: task=123 difficulty=expert achieved=False tier_rate=0.16\n",
+ "16:21:18 | INFO | server.services.curriculum | Episode 60: task=120 difficulty=expert achieved=False tier_rate=0.14\n",
+ "16:21:18 | INFO | server.services.curriculum | Episode 61: task=116 difficulty=expert achieved=False tier_rate=0.12\n",
+ "16:21:18 | INFO | server.services.curriculum | Episode 62: task=122 difficulty=expert achieved=False tier_rate=0.10\n",
+ "16:21:27 | INFO | grpo | [final-grpo] step 4/35 (+208.5s)\n",
+ "16:21:27 | INFO | grpo | [final-grpo] log step=4 {'loss': 0.0373, 'grad_norm': 0.248, 'learning_rate': 0.0, 'num_tokens': 17137.0, 'completions/mean_length': 80.8125, 'completions/min_length': 40.0, 'completions/max_length': 144.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 80.8125, 'completions/min_terminated_length': 40.0, 'completions/max_terminated_length': 144.0, 'rewards/env_reward/mean': 0.2765, 'rewards/env_reward/std': 0.3198, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9406, 'rewards/length_reward/std': 0.1163, 'reward': 2.2171, 'reward_std': 0.3575, 'frac_reward_zero_std': 0.0, 'completion_length': 80.8125, 'kl': 0.1381, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.7619}\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 63: task=135 difficulty=expert achieved=False tier_rate=0.08\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 64: task=24 difficulty=expert achieved=False tier_rate=0.07\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 65: task=128 difficulty=expert achieved=False tier_rate=0.06\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 66: task=18 difficulty=expert achieved=False tier_rate=0.05\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 67: task=123 difficulty=expert achieved=False tier_rate=0.04\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 68: task=25 difficulty=expert achieved=False tier_rate=0.04\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 69: task=127 difficulty=expert achieved=True tier_rate=0.18\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 70: task=120 difficulty=expert achieved=False tier_rate=0.15\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 71: task=131 difficulty=expert achieved=False tier_rate=0.13\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 72: task=23 difficulty=expert achieved=False tier_rate=0.11\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 73: task=126 difficulty=expert achieved=False tier_rate=0.09\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 74: task=119 difficulty=expert achieved=True tier_rate=0.23\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 75: task=110 difficulty=expert achieved=True tier_rate=0.35\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 76: task=134 difficulty=expert achieved=False tier_rate=0.29\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 77: task=22 difficulty=expert achieved=False tier_rate=0.25\n",
+ "16:24:24 | INFO | server.services.curriculum | Episode 78: task=118 difficulty=expert achieved=False tier_rate=0.21\n",
+ "16:24:33 | INFO | grpo | [final-grpo] step 5/35 (+186.4s)\n",
+ "16:24:33 | INFO | grpo | [final-grpo] log step=5 {'loss': 0.0571, 'grad_norm': 0.2858, 'learning_rate': 0.0, 'num_tokens': 21998.0, 'completions/mean_length': 67.25, 'completions/min_length': 16.0, 'completions/max_length': 128.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 67.25, 'completions/min_terminated_length': 16.0, 'completions/max_terminated_length': 128.0, 'rewards/env_reward/mean': 0.2813, 'rewards/env_reward/std': 0.3708, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9493, 'rewards/length_reward/std': 0.1061, 'reward': 2.2307, 'reward_std': 0.4197, 'frac_reward_zero_std': 0.0, 'completion_length': 67.25, 'kl': 0.1941, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 0.9524}\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 79: task=125 difficulty=expert achieved=False tier_rate=0.18\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 80: task=113 difficulty=expert achieved=False tier_rate=0.15\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 81: task=120 difficulty=expert achieved=False tier_rate=0.13\n",
+ "16:27:39 | INFO | server.services.curriculum | Task 119 GRADUATED (rate=1.00) — scheduling spaced repetition\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 82: task=119 difficulty=expert achieved=True tier_rate=0.26\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 83: task=109 difficulty=expert achieved=True tier_rate=0.37\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 84: task=115 difficulty=expert achieved=False tier_rate=0.32\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 85: task=20 difficulty=expert achieved=True tier_rate=0.42\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 86: task=114 difficulty=expert achieved=False tier_rate=0.36\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 87: task=23 difficulty=expert achieved=True tier_rate=0.45\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 88: task=19 difficulty=expert achieved=False tier_rate=0.38\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 89: task=117 difficulty=expert achieved=False tier_rate=0.33\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 90: task=134 difficulty=expert achieved=False tier_rate=0.28\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 91: task=129 difficulty=expert achieved=False tier_rate=0.24\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 92: task=133 difficulty=expert achieved=False tier_rate=0.20\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 93: task=135 difficulty=expert achieved=False tier_rate=0.17\n",
+ "16:27:39 | INFO | server.services.curriculum | Episode 94: task=127 difficulty=expert achieved=False tier_rate=0.15\n",
+ "16:27:42 | INFO | grpo | [final-grpo] step 6/35 (+188.7s)\n",
+ "16:27:42 | INFO | grpo | [final-grpo] log step=6 {'loss': 0.3575, 'grad_norm': 0.4507, 'learning_rate': 0.0, 'num_tokens': 27280.0, 'completions/mean_length': 89.375, 'completions/min_length': 31.0, 'completions/max_length': 165.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 89.375, 'completions/min_terminated_length': 31.0, 'completions/max_terminated_length': 165.0, 'rewards/env_reward/mean': 0.3797, 'rewards/env_reward/std': 0.4109, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9265, 'rewards/length_reward/std': 0.142, 'reward': 2.3062, 'reward_std': 0.4921, 'frac_reward_zero_std': 0.0, 'completion_length': 109.25, 'kl': 0.1111, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 1.0}\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 95: task=131 difficulty=expert achieved=False tier_rate=0.12\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 96: task=117 difficulty=expert achieved=False tier_rate=0.10\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 97: task=114 difficulty=expert achieved=False tier_rate=0.09\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 98: task=110 difficulty=expert achieved=True tier_rate=0.23\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 99: task=18 difficulty=expert achieved=False tier_rate=0.19\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 100: task=115 difficulty=expert achieved=False tier_rate=0.16\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 101: task=124 difficulty=expert achieved=False tier_rate=0.14\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 102: task=20 difficulty=expert achieved=True tier_rate=0.27\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 103: task=118 difficulty=expert achieved=False tier_rate=0.23\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 104: task=113 difficulty=expert achieved=False tier_rate=0.19\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 105: task=121 difficulty=expert achieved=True tier_rate=0.31\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 106: task=23 difficulty=expert achieved=False tier_rate=0.27\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 107: task=24 difficulty=expert achieved=False tier_rate=0.23\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 108: task=139 difficulty=expert achieved=False tier_rate=0.19\n",
+ "16:31:20 | INFO | server.services.curriculum | Episode 109: task=134 difficulty=expert achieved=False tier_rate=0.16\n",
+ "16:31:22 | INFO | grpo | [final-grpo] step 7/35 (+220.6s)\n",
+ "16:31:22 | INFO | grpo | [final-grpo] log step=7 {'loss': 0.1421, 'grad_norm': 0.2291, 'learning_rate': 0.0, 'completion_length': 87.5625, 'kl': 0.1514, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 32677.0, 'completions/mean_length': 97.125, 'completions/min_length': 47.0, 'completions/max_length': 235.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 97.125, 'completions/min_terminated_length': 47.0, 'completions/max_terminated_length': 235.0, 'rewards/env_reward/mean': 0.2553, 'rewards/env_reward/std': 0.3762, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.8959, 'rewards/length_reward/std': 0.1499, 'reward': 2.1511, 'reward_std': 0.4718, 'frac_reward_zero_std': 0.0, 'epoch': 1.1905}\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 110: task=126 difficulty=expert achieved=False tier_rate=0.14\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 111: task=129 difficulty=expert achieved=False tier_rate=0.12\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 112: task=139 difficulty=expert achieved=True tier_rate=0.25\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 113: task=113 difficulty=expert achieved=False tier_rate=0.21\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 114: task=124 difficulty=expert achieved=False tier_rate=0.18\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 115: task=131 difficulty=expert achieved=False tier_rate=0.15\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 116: task=19 difficulty=expert achieved=False tier_rate=0.13\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 117: task=125 difficulty=expert achieved=False tier_rate=0.11\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 118: task=22 difficulty=expert achieved=False tier_rate=0.09\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 119: task=115 difficulty=expert achieved=False tier_rate=0.08\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 120: task=23 difficulty=expert achieved=False tier_rate=0.07\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 121: task=116 difficulty=expert achieved=False tier_rate=0.06\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 122: task=111 difficulty=expert achieved=True tier_rate=0.20\n",
+ "16:35:24 | INFO | server.services.curriculum | Task 20 GRADUATED (rate=1.00) — scheduling spaced repetition\n",
+ "16:35:24 | INFO | server.services.curriculum | Episode 123: task=20 difficulty=expert achieved=True tier_rate=0.32\n",
+ "16:35:26 | INFO | grpo | [final-grpo] step 8/35 (+244.0s)\n",
+ "16:35:26 | INFO | grpo | [final-grpo] log step=8 {'loss': -0.1872, 'grad_norm': 0.3587, 'learning_rate': 0.0, 'completion_length': 80.6875, 'kl': 0.1286, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 38011.0, 'completions/mean_length': 90.1875, 'completions/min_length': 30.0, 'completions/max_length': 191.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 90.1875, 'completions/min_terminated_length': 30.0, 'completions/max_terminated_length': 191.0, 'rewards/env_reward/mean': 0.2475, 'rewards/env_reward/std': 0.3857, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.8772, 'rewards/length_reward/std': 0.1841, 'reward': 2.1247, 'reward_std': 0.5024, 'frac_reward_zero_std': 0.0, 'epoch': 1.381}\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 124: task=125 difficulty=expert achieved=False tier_rate=0.27\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 125: task=128 difficulty=expert achieved=False tier_rate=0.23\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 126: task=119 difficulty=expert achieved=True tier_rate=0.35\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 127: task=24 difficulty=expert achieved=False tier_rate=0.29\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 128: task=109 difficulty=expert achieved=False tier_rate=0.25\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 129: task=123 difficulty=expert achieved=False tier_rate=0.21\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 130: task=134 difficulty=expert achieved=False tier_rate=0.18\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 131: task=122 difficulty=expert achieved=False tier_rate=0.15\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 132: task=135 difficulty=expert achieved=False tier_rate=0.13\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 133: task=110 difficulty=expert achieved=False tier_rate=0.11\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 134: task=115 difficulty=expert achieved=False tier_rate=0.09\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 135: task=111 difficulty=expert achieved=False tier_rate=0.08\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 136: task=126 difficulty=expert achieved=False tier_rate=0.07\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 137: task=23 difficulty=expert achieved=False tier_rate=0.06\n",
+ "16:39:55 | INFO | server.services.curriculum | Episode 138: task=133 difficulty=expert achieved=False tier_rate=0.05\n",
+ "16:39:58 | INFO | grpo | [final-grpo] step 9/35 (+271.4s)\n",
+ "16:39:58 | INFO | grpo | [final-grpo] log step=9 {'loss': 0.3138, 'grad_norm': 0.3186, 'learning_rate': 0.0, 'completion_length': 102.5, 'kl': 0.1146, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 43444.0, 'completions/mean_length': 95.375, 'completions/min_length': 28.0, 'completions/max_length': 304.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 95.375, 'completions/min_terminated_length': 28.0, 'completions/max_terminated_length': 304.0, 'rewards/env_reward/mean': 0.2579, 'rewards/env_reward/std': 0.2712, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9336, 'rewards/length_reward/std': 0.1404, 'reward': 2.1915, 'reward_std': 0.3373, 'frac_reward_zero_std': 0.0, 'epoch': 1.5714}\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 139: task=114 difficulty=expert achieved=False tier_rate=0.04\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 140: task=111 difficulty=expert achieved=False tier_rate=0.04\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 141: task=115 difficulty=expert achieved=False tier_rate=0.03\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 142: task=18 difficulty=expert achieved=False tier_rate=0.03\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 143: task=129 difficulty=expert achieved=False tier_rate=0.02\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 144: task=24 difficulty=expert achieved=False tier_rate=0.02\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 145: task=19 difficulty=expert achieved=False tier_rate=0.02\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 146: task=113 difficulty=expert achieved=False tier_rate=0.01\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 147: task=127 difficulty=expert achieved=False tier_rate=0.01\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 148: task=124 difficulty=expert achieved=False tier_rate=0.01\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 149: task=20 difficulty=expert achieved=True tier_rate=0.16\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 150: task=116 difficulty=expert achieved=False tier_rate=0.13\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 151: task=135 difficulty=expert achieved=False tier_rate=0.11\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 152: task=117 difficulty=expert achieved=False tier_rate=0.10\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 153: task=118 difficulty=expert achieved=False tier_rate=0.08\n",
+ "16:43:37 | INFO | server.services.curriculum | Episode 154: task=119 difficulty=expert achieved=True tier_rate=0.22\n",
+ "16:43:39 | INFO | grpo | [final-grpo] step 10/35 (+221.3s)\n",
+ "16:43:39 | INFO | grpo | [final-grpo] log step=10 {'loss': 0.0866, 'grad_norm': 0.2433, 'learning_rate': 0.0, 'completion_length': 90.8125, 'kl': 0.2182, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 48768.0, 'completions/mean_length': 90.625, 'completions/min_length': 43.0, 'completions/max_length': 164.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 90.625, 'completions/min_terminated_length': 43.0, 'completions/max_terminated_length': 164.0, 'rewards/env_reward/mean': 0.2552, 'rewards/env_reward/std': 0.3453, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9358, 'rewards/length_reward/std': 0.1071, 'reward': 2.1911, 'reward_std': 0.3683, 'frac_reward_zero_std': 0.0, 'epoch': 1.7619}\n",
+ "16:47:05 | INFO | server.services.curriculum | Task 110 GRADUATED (rate=0.73) — scheduling spaced repetition\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 155: task=110 difficulty=expert achieved=True tier_rate=0.34\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 156: task=125 difficulty=expert achieved=False tier_rate=0.29\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 157: task=121 difficulty=expert achieved=False tier_rate=0.24\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 158: task=129 difficulty=expert achieved=False tier_rate=0.21\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 159: task=18 difficulty=expert achieved=False tier_rate=0.18\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 160: task=126 difficulty=expert achieved=False tier_rate=0.15\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 161: task=20 difficulty=expert achieved=True tier_rate=0.28\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 162: task=113 difficulty=expert achieved=False tier_rate=0.24\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 163: task=127 difficulty=expert achieved=True tier_rate=0.35\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 164: task=19 difficulty=expert achieved=False tier_rate=0.30\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 165: task=114 difficulty=expert achieved=False tier_rate=0.25\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 166: task=109 difficulty=expert achieved=False tier_rate=0.22\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 167: task=131 difficulty=expert achieved=False tier_rate=0.18\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 168: task=133 difficulty=expert achieved=False tier_rate=0.16\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 169: task=119 difficulty=expert achieved=True tier_rate=0.28\n",
+ "16:47:05 | INFO | server.services.curriculum | Episode 170: task=139 difficulty=expert achieved=True tier_rate=0.39\n",
+ "16:47:08 | INFO | grpo | [final-grpo] step 11/35 (+208.5s)\n",
+ "16:47:08 | INFO | grpo | [final-grpo] log step=11 {'loss': 0.082, 'grad_norm': 0.1954, 'learning_rate': 0.0, 'completion_length': 98.75, 'kl': 0.161, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 53925.0, 'completions/mean_length': 81.5, 'completions/min_length': 41.0, 'completions/max_length': 194.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 81.5, 'completions/min_terminated_length': 41.0, 'completions/max_terminated_length': 194.0, 'rewards/env_reward/mean': 0.3662, 'rewards/env_reward/std': 0.4547, 'rewards/format_reward/mean': 0.875, 'rewards/format_reward/std': 0.3416, 'rewards/length_reward/mean': 0.9369, 'rewards/length_reward/std': 0.1403, 'reward': 2.1782, 'reward_std': 0.6511, 'frac_reward_zero_std': 0.0, 'epoch': 1.9524}\n",
+ "16:47:10 | INFO | grpo | [final-grpo] step 12/35 (+2.4s)\n",
+ "16:47:10 | INFO | grpo | [final-grpo] log step=12 {'loss': -0.0593, 'grad_norm': 0.2592, 'learning_rate': 0.0, 'completion_length': 71.75, 'kl': 0.1245, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 2.0}\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 171: task=128 difficulty=expert achieved=False tier_rate=0.33\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 172: task=133 difficulty=expert achieved=False tier_rate=0.28\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 173: task=139 difficulty=expert achieved=False tier_rate=0.24\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 174: task=123 difficulty=expert achieved=True tier_rate=0.35\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 175: task=127 difficulty=expert achieved=True tier_rate=0.45\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 176: task=109 difficulty=expert achieved=False tier_rate=0.38\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 177: task=120 difficulty=expert achieved=False tier_rate=0.33\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 178: task=25 difficulty=expert achieved=False tier_rate=0.28\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 179: task=115 difficulty=expert achieved=False tier_rate=0.24\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 180: task=125 difficulty=expert achieved=False tier_rate=0.20\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 181: task=23 difficulty=expert achieved=False tier_rate=0.17\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 182: task=121 difficulty=expert achieved=False tier_rate=0.14\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 183: task=113 difficulty=expert achieved=False tier_rate=0.12\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 184: task=114 difficulty=expert achieved=False tier_rate=0.10\n",
+ "16:50:32 | INFO | server.services.curriculum | Episode 185: task=21 difficulty=expert achieved=False tier_rate=0.09\n",
+ "16:50:37 | INFO | grpo | [final-grpo] step 13/35 (+206.5s)\n",
+ "16:50:37 | INFO | grpo | [final-grpo] log step=13 {'loss': 0.1211, 'grad_norm': 0.3559, 'learning_rate': 0.0, 'completion_length': 73.3125, 'kl': 0.1532, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 59226.0, 'completions/mean_length': 85.5625, 'completions/min_length': 32.0, 'completions/max_length': 182.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 85.5625, 'completions/min_terminated_length': 32.0, 'completions/max_terminated_length': 182.0, 'rewards/env_reward/mean': 0.2295, 'rewards/env_reward/std': 0.333, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9407, 'rewards/length_reward/std': 0.0867, 'reward': 2.1703, 'reward_std': 0.3273, 'frac_reward_zero_std': 0.0, 'epoch': 2.1905}\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 186: task=114 difficulty=expert achieved=False tier_rate=0.08\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 187: task=139 difficulty=expert achieved=True tier_rate=0.21\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 188: task=113 difficulty=expert achieved=False tier_rate=0.18\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 189: task=128 difficulty=expert achieved=False tier_rate=0.15\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 190: task=120 difficulty=expert achieved=False tier_rate=0.13\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 191: task=135 difficulty=expert achieved=False tier_rate=0.11\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 192: task=21 difficulty=expert achieved=True tier_rate=0.24\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 193: task=117 difficulty=expert achieved=False tier_rate=0.21\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 194: task=19 difficulty=expert achieved=False tier_rate=0.18\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 195: task=116 difficulty=expert achieved=False tier_rate=0.15\n",
+ "16:53:50 | INFO | server.services.curriculum | Task 127 GRADUATED (rate=0.73) — scheduling spaced repetition\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 196: task=127 difficulty=expert achieved=True tier_rate=0.28\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 197: task=110 difficulty=expert achieved=True tier_rate=0.39\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 198: task=24 difficulty=expert achieved=False tier_rate=0.33\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 199: task=124 difficulty=expert achieved=False tier_rate=0.28\n",
+ "16:53:50 | INFO | server.services.curriculum | Episode 200: task=134 difficulty=expert achieved=False tier_rate=0.24\n",
+ "16:53:55 | INFO | grpo | [final-grpo] step 14/35 (+198.0s)\n",
+ "16:53:55 | INFO | grpo | [final-grpo] log step=14 {'loss': 0.0968, 'grad_norm': 0.252, 'learning_rate': 0.0, 'completion_length': 100.5625, 'kl': 0.1398, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 64542.0, 'completions/mean_length': 90.5, 'completions/min_length': 43.0, 'completions/max_length': 146.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 90.5, 'completions/min_terminated_length': 43.0, 'completions/max_terminated_length': 146.0, 'rewards/env_reward/mean': 0.3662, 'rewards/env_reward/std': 0.4132, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9135, 'rewards/length_reward/std': 0.1215, 'reward': 2.2797, 'reward_std': 0.4782, 'frac_reward_zero_std': 0.0, 'epoch': 2.381}\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 201: task=133 difficulty=expert achieved=False tier_rate=0.20\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 202: task=109 difficulty=expert achieved=False tier_rate=0.17\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 203: task=115 difficulty=expert achieved=False tier_rate=0.15\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 204: task=139 difficulty=expert achieved=False tier_rate=0.12\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 205: task=121 difficulty=expert achieved=False tier_rate=0.11\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 206: task=113 difficulty=expert achieved=False tier_rate=0.09\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 207: task=114 difficulty=expert achieved=False tier_rate=0.08\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 208: task=120 difficulty=expert achieved=False tier_rate=0.06\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 209: task=134 difficulty=expert achieved=False tier_rate=0.05\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 210: task=122 difficulty=expert achieved=False tier_rate=0.05\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 211: task=20 difficulty=expert achieved=True tier_rate=0.19\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 212: task=117 difficulty=expert achieved=False tier_rate=0.16\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 213: task=116 difficulty=expert achieved=False tier_rate=0.14\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 214: task=110 difficulty=expert achieved=True tier_rate=0.27\n",
+ "16:57:43 | INFO | server.services.curriculum | Episode 215: task=126 difficulty=expert achieved=False tier_rate=0.23\n",
+ "16:57:48 | INFO | grpo | [final-grpo] step 15/35 (+233.5s)\n",
+ "16:57:48 | INFO | grpo | [final-grpo] log step=15 {'loss': -0.0866, 'grad_norm': 0.2542, 'learning_rate': 0.0, 'completion_length': 84.375, 'kl': 0.1345, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 69954.0, 'completions/mean_length': 94.6875, 'completions/min_length': 35.0, 'completions/max_length': 190.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 94.6875, 'completions/min_terminated_length': 35.0, 'completions/max_terminated_length': 190.0, 'rewards/env_reward/mean': 0.2557, 'rewards/env_reward/std': 0.3671, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9046, 'rewards/length_reward/std': 0.1519, 'reward': 2.1602, 'reward_std': 0.4395, 'frac_reward_zero_std': 0.0, 'epoch': 2.5714}\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 216: task=116 difficulty=expert achieved=False tier_rate=0.19\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 217: task=124 difficulty=expert achieved=False tier_rate=0.16\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 218: task=126 difficulty=expert achieved=False tier_rate=0.14\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 219: task=131 difficulty=expert achieved=True tier_rate=0.27\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 220: task=23 difficulty=expert achieved=False tier_rate=0.23\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 221: task=134 difficulty=expert achieved=False tier_rate=0.19\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 222: task=113 difficulty=expert achieved=False tier_rate=0.16\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 223: task=123 difficulty=expert achieved=False tier_rate=0.14\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 224: task=139 difficulty=expert achieved=False tier_rate=0.12\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 225: task=21 difficulty=expert achieved=False tier_rate=0.10\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 226: task=109 difficulty=expert achieved=False tier_rate=0.09\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 227: task=18 difficulty=expert achieved=False tier_rate=0.07\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 228: task=111 difficulty=expert achieved=False tier_rate=0.06\n",
+ "17:01:26 | INFO | server.services.curriculum | Episode 229: task=24 difficulty=expert achieved=False tier_rate=0.05\n",
+ "17:01:30 | INFO | grpo | [final-grpo] step 16/35 (+222.3s)\n",
+ "17:01:30 | INFO | grpo | [final-grpo] log step=16 {'loss': 0.2731, 'grad_norm': 0.221, 'learning_rate': 0.0, 'completion_length': 99.0, 'kl': 0.0956, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 75206.0, 'completions/mean_length': 85.25, 'completions/min_length': 41.0, 'completions/max_length': 173.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 85.25, 'completions/min_terminated_length': 41.0, 'completions/max_terminated_length': 173.0, 'rewards/env_reward/mean': 0.1604, 'rewards/env_reward/std': 0.2648, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9202, 'rewards/length_reward/std': 0.1367, 'reward': 2.0806, 'reward_std': 0.3175, 'frac_reward_zero_std': 0.0, 'epoch': 2.7619}\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 230: task=115 difficulty=expert achieved=False tier_rate=0.04\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 231: task=114 difficulty=expert achieved=False tier_rate=0.04\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 232: task=125 difficulty=expert achieved=False tier_rate=0.03\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 233: task=128 difficulty=expert achieved=False tier_rate=0.03\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 234: task=135 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 235: task=134 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 236: task=133 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 237: task=126 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 238: task=23 difficulty=expert achieved=True tier_rate=0.16\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 239: task=110 difficulty=expert achieved=True tier_rate=0.29\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 240: task=117 difficulty=expert achieved=False tier_rate=0.24\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 241: task=19 difficulty=expert achieved=False tier_rate=0.21\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 242: task=109 difficulty=expert achieved=False tier_rate=0.18\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 243: task=119 difficulty=expert achieved=True tier_rate=0.30\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 244: task=124 difficulty=expert achieved=False tier_rate=0.26\n",
+ "17:05:28 | INFO | server.services.curriculum | Episode 245: task=18 difficulty=expert achieved=False tier_rate=0.22\n",
+ "17:05:32 | INFO | grpo | [final-grpo] step 17/35 (+241.9s)\n",
+ "17:05:32 | INFO | grpo | [final-grpo] log step=17 {'loss': 0.1019, 'grad_norm': 0.2589, 'learning_rate': 0.0, 'completion_length': 87.6875, 'kl': 0.1146, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 80407.0, 'completions/mean_length': 87.9375, 'completions/min_length': 31.0, 'completions/max_length': 167.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 87.9375, 'completions/min_terminated_length': 31.0, 'completions/max_terminated_length': 167.0, 'rewards/env_reward/mean': 0.2371, 'rewards/env_reward/std': 0.3825, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.917, 'rewards/length_reward/std': 0.1256, 'reward': 2.1541, 'reward_std': 0.3543, 'frac_reward_zero_std': 0.0, 'epoch': 2.9524}\n",
+ "17:05:35 | INFO | grpo | [final-grpo] step 18/35 (+2.3s)\n",
+ "17:05:35 | INFO | grpo | [final-grpo] log step=18 {'loss': 0.2207, 'grad_norm': 0.5284, 'learning_rate': 0.0, 'completion_length': 79.0, 'kl': 0.134, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 3.0}\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 246: task=126 difficulty=expert achieved=False tier_rate=0.18\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 247: task=109 difficulty=expert achieved=False tier_rate=0.16\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 248: task=23 difficulty=expert achieved=False tier_rate=0.13\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 249: task=129 difficulty=expert achieved=False tier_rate=0.11\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 250: task=24 difficulty=expert achieved=False tier_rate=0.10\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 251: task=118 difficulty=expert achieved=False tier_rate=0.08\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 252: task=119 difficulty=expert achieved=True tier_rate=0.22\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 253: task=22 difficulty=expert achieved=False tier_rate=0.19\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 254: task=18 difficulty=expert achieved=False tier_rate=0.16\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 255: task=139 difficulty=expert achieved=True tier_rate=0.28\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 256: task=21 difficulty=expert achieved=True tier_rate=0.39\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 257: task=133 difficulty=expert achieved=False tier_rate=0.33\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 258: task=122 difficulty=expert achieved=False tier_rate=0.28\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 259: task=110 difficulty=expert achieved=True tier_rate=0.39\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 260: task=123 difficulty=expert achieved=False tier_rate=0.33\n",
+ "17:08:52 | INFO | server.services.curriculum | Episode 261: task=115 difficulty=expert achieved=False tier_rate=0.28\n",
+ "17:09:00 | INFO | grpo | [final-grpo] step 19/35 (+205.3s)\n",
+ "17:09:00 | INFO | grpo | [final-grpo] log step=19 {'loss': -0.1852, 'grad_norm': 0.2376, 'learning_rate': 0.0, 'completion_length': 89.8125, 'kl': 0.1185, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 85782.0, 'completions/mean_length': 97.9375, 'completions/min_length': 44.0, 'completions/max_length': 264.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 97.9375, 'completions/min_terminated_length': 44.0, 'completions/max_terminated_length': 264.0, 'rewards/env_reward/mean': 0.3362, 'rewards/env_reward/std': 0.4117, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9021, 'rewards/length_reward/std': 0.1455, 'reward': 2.2383, 'reward_std': 0.4771, 'frac_reward_zero_std': 0.0, 'epoch': 3.1905}\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 262: task=119 difficulty=expert achieved=False tier_rate=0.24\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 263: task=111 difficulty=expert achieved=True tier_rate=0.35\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 264: task=124 difficulty=expert achieved=False tier_rate=0.30\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 265: task=25 difficulty=expert achieved=False tier_rate=0.26\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 266: task=120 difficulty=expert achieved=False tier_rate=0.22\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 267: task=128 difficulty=expert achieved=False tier_rate=0.18\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 268: task=122 difficulty=expert achieved=False tier_rate=0.16\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 269: task=126 difficulty=expert achieved=False tier_rate=0.13\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 270: task=19 difficulty=expert achieved=False tier_rate=0.11\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 271: task=115 difficulty=expert achieved=False tier_rate=0.10\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 272: task=114 difficulty=expert achieved=False tier_rate=0.08\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 273: task=135 difficulty=expert achieved=False tier_rate=0.07\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 274: task=123 difficulty=expert achieved=False tier_rate=0.06\n",
+ "17:12:16 | INFO | server.services.curriculum | Episode 275: task=134 difficulty=expert achieved=False tier_rate=0.05\n",
+ "17:12:24 | INFO | grpo | [final-grpo] step 20/35 (+203.7s)\n",
+ "17:12:24 | INFO | grpo | [final-grpo] log step=20 {'loss': 0.0928, 'grad_norm': 0.2356, 'learning_rate': 0.0, 'completion_length': 74.3125, 'kl': 0.1807, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 90979.0, 'completions/mean_length': 78.0, 'completions/min_length': 17.0, 'completions/max_length': 193.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 78.0, 'completions/min_terminated_length': 17.0, 'completions/max_terminated_length': 193.0, 'rewards/env_reward/mean': 0.2499, 'rewards/env_reward/std': 0.2785, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.954, 'rewards/length_reward/std': 0.1011, 'reward': 2.2039, 'reward_std': 0.3256, 'frac_reward_zero_std': 0.0, 'epoch': 3.381}\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 276: task=19 difficulty=expert achieved=False tier_rate=0.04\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 277: task=134 difficulty=expert achieved=False tier_rate=0.04\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 278: task=121 difficulty=expert achieved=False tier_rate=0.03\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 279: task=21 difficulty=expert achieved=False tier_rate=0.03\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 280: task=25 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 281: task=131 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 282: task=24 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 283: task=114 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 284: task=115 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 285: task=139 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 286: task=23 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 287: task=125 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 288: task=128 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 289: task=113 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:16:21 | INFO | server.services.curriculum | Episode 290: task=116 difficulty=expert achieved=False tier_rate=0.00\n",
+ "17:16:28 | INFO | grpo | [final-grpo] step 21/35 (+244.0s)\n",
+ "17:16:28 | INFO | grpo | [final-grpo] log step=21 {'loss': 0.4222, 'grad_norm': 0.2285, 'learning_rate': 0.0, 'completion_length': 107.375, 'kl': 0.1115, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 96292.0, 'completions/mean_length': 89.8125, 'completions/min_length': 38.0, 'completions/max_length': 153.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 89.8125, 'completions/min_terminated_length': 38.0, 'completions/max_terminated_length': 153.0, 'rewards/env_reward/mean': 0.1265, 'rewards/env_reward/std': 0.1237, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9045, 'rewards/length_reward/std': 0.1363, 'reward': 2.031, 'reward_std': 0.2181, 'frac_reward_zero_std': 0.0, 'epoch': 3.5714}\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 291: task=129 difficulty=expert achieved=False tier_rate=0.00\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 292: task=133 difficulty=expert achieved=False tier_rate=0.00\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 293: task=119 difficulty=expert achieved=True tier_rate=0.15\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 294: task=128 difficulty=expert achieved=False tier_rate=0.13\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 295: task=134 difficulty=expert achieved=False tier_rate=0.11\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 296: task=123 difficulty=expert achieved=False tier_rate=0.09\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 297: task=19 difficulty=expert achieved=False tier_rate=0.08\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 298: task=118 difficulty=expert achieved=False tier_rate=0.07\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 299: task=18 difficulty=expert achieved=False tier_rate=0.06\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 300: task=114 difficulty=expert achieved=False tier_rate=0.05\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 301: task=124 difficulty=expert achieved=False tier_rate=0.04\n",
+ "17:19:54 | INFO | server.services.curriculum | Task 127 UN-GRADUATED (rate=0.57) — resetting to active\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 302: task=127 difficulty=expert achieved=False tier_rate=0.04\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 303: task=111 difficulty=expert achieved=False tier_rate=0.03\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 304: task=121 difficulty=expert achieved=False tier_rate=0.03\n",
+ "17:19:54 | INFO | server.services.curriculum | Episode 305: task=110 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:20:02 | INFO | grpo | [final-grpo] step 22/35 (+214.2s)\n",
+ "17:20:02 | INFO | grpo | [final-grpo] log step=22 {'loss': -0.0373, 'grad_norm': 0.2382, 'learning_rate': 0.0, 'completion_length': 83.25, 'kl': 0.1369, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 101634.0, 'completions/mean_length': 93.9375, 'completions/min_length': 28.0, 'completions/max_length': 291.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 93.9375, 'completions/min_terminated_length': 28.0, 'completions/max_terminated_length': 291.0, 'rewards/env_reward/mean': 0.3159, 'rewards/env_reward/std': 0.3495, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9655, 'rewards/length_reward/std': 0.0714, 'reward': 2.2814, 'reward_std': 0.3936, 'frac_reward_zero_std': 0.0, 'epoch': 3.7619}\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 306: task=117 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 307: task=125 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 308: task=19 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 309: task=123 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 310: task=118 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 311: task=111 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 312: task=129 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 313: task=22 difficulty=expert achieved=True tier_rate=0.16\n",
+ "17:24:01 | INFO | server.services.curriculum | Task 110 UN-GRADUATED (rate=0.59) — resetting to active\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 314: task=110 difficulty=expert achieved=False tier_rate=0.13\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 315: task=115 difficulty=expert achieved=False tier_rate=0.11\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 316: task=109 difficulty=expert achieved=False tier_rate=0.10\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 317: task=18 difficulty=expert achieved=False tier_rate=0.08\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 318: task=133 difficulty=expert achieved=False tier_rate=0.07\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 319: task=128 difficulty=expert achieved=False tier_rate=0.06\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 320: task=120 difficulty=expert achieved=False tier_rate=0.05\n",
+ "17:24:01 | INFO | server.services.curriculum | Episode 321: task=126 difficulty=expert achieved=False tier_rate=0.04\n",
+ "17:24:09 | INFO | grpo | [final-grpo] step 23/35 (+247.2s)\n",
+ "17:24:09 | INFO | grpo | [final-grpo] log step=23 {'loss': 0.1686, 'grad_norm': 0.2184, 'learning_rate': 0.0, 'completion_length': 105.1875, 'kl': 0.1559, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 107148.0, 'completions/mean_length': 105.1875, 'completions/min_length': 41.0, 'completions/max_length': 303.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 105.1875, 'completions/min_terminated_length': 41.0, 'completions/max_terminated_length': 303.0, 'rewards/env_reward/mean': 0.1881, 'rewards/env_reward/std': 0.2464, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9008, 'rewards/length_reward/std': 0.1619, 'reward': 2.0889, 'reward_std': 0.334, 'frac_reward_zero_std': 0.0, 'epoch': 3.9524}\n",
+ "17:24:12 | INFO | grpo | [final-grpo] step 24/35 (+2.9s)\n",
+ "17:24:12 | INFO | grpo | [final-grpo] log step=24 {'loss': -0.0629, 'grad_norm': 0.1749, 'learning_rate': 0.0, 'completion_length': 91.25, 'kl': 0.1545, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.0}\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 322: task=20 difficulty=expert achieved=True tier_rate=0.19\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 323: task=134 difficulty=expert achieved=False tier_rate=0.16\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 324: task=18 difficulty=expert achieved=False tier_rate=0.13\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 325: task=126 difficulty=expert achieved=False tier_rate=0.11\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 326: task=121 difficulty=expert achieved=False tier_rate=0.10\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 327: task=127 difficulty=expert achieved=True tier_rate=0.23\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 328: task=19 difficulty=expert achieved=False tier_rate=0.20\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 329: task=25 difficulty=expert achieved=False tier_rate=0.17\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 330: task=131 difficulty=expert achieved=False tier_rate=0.14\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 331: task=23 difficulty=expert achieved=False tier_rate=0.12\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 332: task=21 difficulty=expert achieved=False tier_rate=0.10\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 333: task=119 difficulty=expert achieved=True tier_rate=0.24\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 334: task=128 difficulty=expert achieved=False tier_rate=0.20\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 335: task=118 difficulty=expert achieved=False tier_rate=0.17\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 336: task=116 difficulty=expert achieved=False tier_rate=0.15\n",
+ "17:27:15 | INFO | server.services.curriculum | Episode 337: task=22 difficulty=expert achieved=True tier_rate=0.27\n",
+ "17:27:25 | INFO | grpo | [final-grpo] step 25/35 (+193.1s)\n",
+ "17:27:25 | INFO | grpo | [final-grpo] log step=25 {'loss': 0.0519, 'grad_norm': 0.2533, 'learning_rate': 0.0, 'num_tokens': 112197.0, 'completions/mean_length': 83.3125, 'completions/min_length': 36.0, 'completions/max_length': 252.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 83.3125, 'completions/min_terminated_length': 36.0, 'completions/max_terminated_length': 252.0, 'rewards/env_reward/mean': 0.3733, 'rewards/env_reward/std': 0.4133, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9321, 'rewards/length_reward/std': 0.1404, 'reward': 2.3054, 'reward_std': 0.4664, 'frac_reward_zero_std': 0.0, 'completion_length': 83.3125, 'kl': 0.1324, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.1905}\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 338: task=115 difficulty=expert achieved=False tier_rate=0.23\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 339: task=126 difficulty=expert achieved=False tier_rate=0.20\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 340: task=122 difficulty=expert achieved=False tier_rate=0.17\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 341: task=118 difficulty=expert achieved=False tier_rate=0.14\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 342: task=133 difficulty=expert achieved=False tier_rate=0.12\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 343: task=129 difficulty=expert achieved=False tier_rate=0.10\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 344: task=114 difficulty=expert achieved=False tier_rate=0.09\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 345: task=18 difficulty=expert achieved=False tier_rate=0.07\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 346: task=116 difficulty=expert achieved=False tier_rate=0.06\n",
+ "17:33:40 | INFO | server.services.curriculum | Task 127 GRADUATED (rate=0.72) — scheduling spaced repetition\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 347: task=127 difficulty=expert achieved=True tier_rate=0.20\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 348: task=25 difficulty=expert achieved=False tier_rate=0.17\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 349: task=110 difficulty=expert achieved=True tier_rate=0.30\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 350: task=21 difficulty=expert achieved=True tier_rate=0.40\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 351: task=128 difficulty=expert achieved=False tier_rate=0.34\n",
+ "17:33:40 | INFO | server.services.curriculum | Episode 352: task=123 difficulty=expert achieved=False tier_rate=0.29\n",
+ "17:33:59 | INFO | grpo | [final-grpo] step 26/35 (+394.3s)\n",
+ "17:33:59 | INFO | grpo | [final-grpo] log step=26 {'loss': 0.0847, 'grad_norm': 0.2684, 'learning_rate': 0.0, 'num_tokens': 118063.0, 'completions/mean_length': 128.1875, 'completions/min_length': 42.0, 'completions/max_length': 768.0, 'completions/clipped_ratio': 0.0625, 'completions/mean_terminated_length': 85.5333, 'completions/min_terminated_length': 42.0, 'completions/max_terminated_length': 154.0, 'rewards/env_reward/mean': 0.2754, 'rewards/env_reward/std': 0.3726, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.8385, 'rewards/length_reward/std': 0.1988, 'reward': 2.1139, 'reward_std': 0.4257, 'frac_reward_zero_std': 0.0, 'completion_length': 128.1875, 'kl': 0.161, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.381}\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 353: task=127 difficulty=expert achieved=True tier_rate=0.40\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 354: task=19 difficulty=expert achieved=False tier_rate=0.34\n",
+ "17:37:05 | INFO | server.services.curriculum | Task 110 GRADUATED (rate=0.71) — scheduling spaced repetition\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 355: task=110 difficulty=expert achieved=True tier_rate=0.44\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 356: task=126 difficulty=expert achieved=False tier_rate=0.37\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 357: task=123 difficulty=expert achieved=True tier_rate=0.47\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 358: task=131 difficulty=expert achieved=False tier_rate=0.40\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 359: task=115 difficulty=expert achieved=False tier_rate=0.34\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 360: task=24 difficulty=expert achieved=False tier_rate=0.29\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 361: task=117 difficulty=expert achieved=False tier_rate=0.24\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 362: task=21 difficulty=expert achieved=False tier_rate=0.21\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 363: task=129 difficulty=expert achieved=False tier_rate=0.18\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 364: task=111 difficulty=expert achieved=False tier_rate=0.15\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 365: task=125 difficulty=expert achieved=False tier_rate=0.13\n",
+ "17:37:05 | INFO | server.services.curriculum | Episode 366: task=124 difficulty=expert achieved=False tier_rate=0.11\n",
+ "17:37:14 | INFO | grpo | [final-grpo] step 27/35 (+195.2s)\n",
+ "17:37:14 | INFO | grpo | [final-grpo] log step=27 {'loss': 0.11, 'grad_norm': 0.1977, 'learning_rate': 0.0, 'num_tokens': 123203.0, 'completions/mean_length': 76.5625, 'completions/min_length': 25.0, 'completions/max_length': 145.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 76.5625, 'completions/min_terminated_length': 25.0, 'completions/max_terminated_length': 145.0, 'rewards/env_reward/mean': 0.3355, 'rewards/env_reward/std': 0.4122, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9378, 'rewards/length_reward/std': 0.0952, 'reward': 2.2734, 'reward_std': 0.4548, 'frac_reward_zero_std': 0.0, 'completion_length': 76.5625, 'kl': 0.1915, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.5714}\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 367: task=123 difficulty=expert achieved=False tier_rate=0.09\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 368: task=125 difficulty=expert achieved=False tier_rate=0.08\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 369: task=24 difficulty=expert achieved=False tier_rate=0.07\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 370: task=19 difficulty=expert achieved=False tier_rate=0.06\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 371: task=22 difficulty=expert achieved=False tier_rate=0.05\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 372: task=117 difficulty=expert achieved=False tier_rate=0.04\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 373: task=124 difficulty=expert achieved=False tier_rate=0.03\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 374: task=114 difficulty=expert achieved=False tier_rate=0.03\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 375: task=23 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 376: task=21 difficulty=expert achieved=True tier_rate=0.17\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 377: task=121 difficulty=expert achieved=False tier_rate=0.15\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 378: task=116 difficulty=expert achieved=False tier_rate=0.12\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 379: task=139 difficulty=expert achieved=False tier_rate=0.11\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 380: task=25 difficulty=expert achieved=False tier_rate=0.09\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 381: task=115 difficulty=expert achieved=False tier_rate=0.08\n",
+ "17:41:08 | INFO | server.services.curriculum | Episode 382: task=126 difficulty=expert achieved=False tier_rate=0.06\n",
+ "17:41:17 | INFO | grpo | [final-grpo] step 28/35 (+243.0s)\n",
+ "17:41:17 | INFO | grpo | [final-grpo] log step=28 {'loss': -0.054, 'grad_norm': 0.2198, 'learning_rate': 0.0, 'num_tokens': 128577.0, 'completions/mean_length': 95.25, 'completions/min_length': 29.0, 'completions/max_length': 170.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 95.25, 'completions/min_terminated_length': 29.0, 'completions/max_terminated_length': 170.0, 'rewards/env_reward/mean': 0.2246, 'rewards/env_reward/std': 0.3104, 'rewards/format_reward/mean': 0.9375, 'rewards/format_reward/std': 0.25, 'rewards/length_reward/mean': 0.908, 'rewards/length_reward/std': 0.1206, 'reward': 2.0701, 'reward_std': 0.4388, 'frac_reward_zero_std': 0.0, 'completion_length': 95.25, 'kl': 0.156, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.7619}\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 383: task=23 difficulty=expert achieved=False tier_rate=0.05\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 384: task=120 difficulty=expert achieved=False tier_rate=0.05\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 385: task=111 difficulty=expert achieved=False tier_rate=0.04\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 386: task=110 difficulty=expert achieved=True tier_rate=0.18\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 387: task=134 difficulty=expert achieved=False tier_rate=0.16\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 388: task=135 difficulty=expert achieved=False tier_rate=0.13\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 389: task=19 difficulty=expert achieved=False tier_rate=0.11\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 390: task=128 difficulty=expert achieved=False tier_rate=0.10\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 391: task=114 difficulty=expert achieved=False tier_rate=0.08\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 392: task=20 difficulty=expert achieved=False tier_rate=0.07\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 393: task=129 difficulty=expert achieved=False tier_rate=0.06\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 394: task=139 difficulty=expert achieved=False tier_rate=0.05\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 395: task=25 difficulty=expert achieved=False tier_rate=0.04\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 396: task=131 difficulty=expert achieved=False tier_rate=0.04\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 397: task=113 difficulty=expert achieved=False tier_rate=0.03\n",
+ "17:44:43 | INFO | server.services.curriculum | Episode 398: task=115 difficulty=expert achieved=False tier_rate=0.03\n",
+ "17:44:53 | INFO | grpo | [final-grpo] step 29/35 (+215.2s)\n",
+ "17:44:53 | INFO | grpo | [final-grpo] log step=29 {'loss': 0.1324, 'grad_norm': 0.3037, 'learning_rate': 0.0, 'num_tokens': 133793.0, 'completions/mean_length': 81.9375, 'completions/min_length': 24.0, 'completions/max_length': 168.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 81.9375, 'completions/min_terminated_length': 24.0, 'completions/max_terminated_length': 168.0, 'rewards/env_reward/mean': 0.2174, 'rewards/env_reward/std': 0.2527, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9206, 'rewards/length_reward/std': 0.1428, 'reward': 2.138, 'reward_std': 0.3027, 'frac_reward_zero_std': 0.0, 'completion_length': 81.9375, 'kl': 0.1395, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 4.9524}\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 399: task=23 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 400: task=126 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 401: task=19 difficulty=expert achieved=False tier_rate=0.02\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 402: task=115 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 403: task=116 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 404: task=128 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 405: task=133 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 406: task=22 difficulty=expert achieved=False tier_rate=0.01\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 407: task=110 difficulty=expert achieved=True tier_rate=0.16\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 408: task=135 difficulty=expert achieved=False tier_rate=0.13\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 409: task=134 difficulty=expert achieved=True tier_rate=0.26\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 410: task=25 difficulty=expert achieved=False tier_rate=0.22\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 411: task=124 difficulty=expert achieved=False tier_rate=0.19\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 412: task=139 difficulty=expert achieved=False tier_rate=0.16\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 413: task=18 difficulty=expert achieved=False tier_rate=0.14\n",
+ "17:48:07 | INFO | server.services.curriculum | Episode 414: task=117 difficulty=expert achieved=False tier_rate=0.12\n",
+ "17:48:09 | INFO | grpo | [final-grpo] step 30/35 (+196.5s)\n",
+ "17:48:09 | INFO | grpo | [final-grpo] log step=30 {'loss': 0.6377, 'grad_norm': 0.2491, 'learning_rate': 0.0, 'num_tokens': 138737.0, 'completions/mean_length': 73.1875, 'completions/min_length': 38.0, 'completions/max_length': 151.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 73.1875, 'completions/min_terminated_length': 38.0, 'completions/max_terminated_length': 151.0, 'rewards/env_reward/mean': 0.2316, 'rewards/env_reward/std': 0.3266, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9502, 'rewards/length_reward/std': 0.087, 'reward': 2.1818, 'reward_std': 0.3468, 'frac_reward_zero_std': 0.0, 'completion_length': 84.25, 'kl': 0.1716, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'epoch': 5.0}\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 415: task=123 difficulty=expert achieved=False tier_rate=0.10\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 416: task=124 difficulty=expert achieved=False tier_rate=0.08\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 417: task=134 difficulty=expert achieved=False tier_rate=0.07\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 418: task=23 difficulty=expert achieved=True tier_rate=0.21\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 419: task=111 difficulty=expert achieved=False tier_rate=0.18\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 420: task=120 difficulty=expert achieved=False tier_rate=0.15\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 421: task=118 difficulty=expert achieved=False tier_rate=0.13\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 422: task=129 difficulty=expert achieved=False tier_rate=0.11\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 423: task=18 difficulty=expert achieved=False tier_rate=0.09\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 424: task=128 difficulty=expert achieved=False tier_rate=0.08\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 425: task=22 difficulty=expert achieved=True tier_rate=0.22\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 426: task=121 difficulty=expert achieved=False tier_rate=0.18\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 427: task=113 difficulty=expert achieved=False tier_rate=0.16\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 428: task=139 difficulty=expert achieved=True tier_rate=0.28\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 429: task=25 difficulty=expert achieved=False tier_rate=0.24\n",
+ "17:51:37 | INFO | server.services.curriculum | Episode 430: task=20 difficulty=expert achieved=True tier_rate=0.35\n",
+ "17:51:40 | INFO | grpo | [final-grpo] step 31/35 (+210.4s)\n",
+ "17:51:40 | INFO | grpo | [final-grpo] log step=31 {'loss': -0.1123, 'grad_norm': 0.2871, 'learning_rate': 0.0, 'completion_length': 70.0625, 'kl': 0.1626, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 144036.0, 'completions/mean_length': 88.8125, 'completions/min_length': 47.0, 'completions/max_length': 147.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 88.8125, 'completions/min_terminated_length': 47.0, 'completions/max_terminated_length': 147.0, 'rewards/env_reward/mean': 0.3949, 'rewards/env_reward/std': 0.4188, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9569, 'rewards/length_reward/std': 0.1044, 'reward': 2.3518, 'reward_std': 0.4425, 'frac_reward_zero_std': 0.0, 'epoch': 5.1905}\n",
+ "17:55:22 | INFO | server.services.curriculum | Task 127 UN-GRADUATED (rate=0.62) — resetting to active\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 431: task=127 difficulty=expert achieved=False tier_rate=0.30\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 432: task=120 difficulty=expert achieved=False tier_rate=0.26\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 433: task=111 difficulty=expert achieved=True tier_rate=0.37\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 434: task=133 difficulty=expert achieved=False tier_rate=0.31\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 435: task=118 difficulty=expert achieved=False tier_rate=0.27\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 436: task=124 difficulty=expert achieved=False tier_rate=0.23\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 437: task=115 difficulty=expert achieved=False tier_rate=0.19\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 438: task=114 difficulty=expert achieved=False tier_rate=0.16\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 439: task=25 difficulty=expert achieved=False tier_rate=0.14\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 440: task=110 difficulty=expert achieved=True tier_rate=0.27\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 441: task=20 difficulty=expert achieved=True tier_rate=0.38\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 442: task=113 difficulty=expert achieved=False tier_rate=0.32\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 443: task=126 difficulty=expert achieved=False tier_rate=0.27\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 444: task=123 difficulty=expert achieved=False tier_rate=0.23\n",
+ "17:55:22 | INFO | server.services.curriculum | Episode 445: task=117 difficulty=expert achieved=False tier_rate=0.20\n",
+ "17:55:24 | INFO | grpo | [final-grpo] step 32/35 (+224.6s)\n",
+ "17:55:24 | INFO | grpo | [final-grpo] log step=32 {'loss': 0.1634, 'grad_norm': 0.3088, 'learning_rate': 0.0, 'completion_length': 101.25, 'kl': 0.1422, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 149315.0, 'completions/mean_length': 83.9375, 'completions/min_length': 29.0, 'completions/max_length': 182.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 83.9375, 'completions/min_terminated_length': 29.0, 'completions/max_terminated_length': 182.0, 'rewards/env_reward/mean': 0.2958, 'rewards/env_reward/std': 0.3892, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9025, 'rewards/length_reward/std': 0.1382, 'reward': 2.1983, 'reward_std': 0.4893, 'frac_reward_zero_std': 0.0, 'epoch': 5.381}\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 446: task=131 difficulty=expert achieved=False tier_rate=0.17\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 447: task=110 difficulty=expert achieved=True tier_rate=0.29\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 448: task=111 difficulty=expert achieved=False tier_rate=0.25\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 449: task=21 difficulty=expert achieved=False tier_rate=0.21\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 450: task=113 difficulty=expert achieved=False tier_rate=0.18\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 451: task=125 difficulty=expert achieved=False tier_rate=0.15\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 452: task=118 difficulty=expert achieved=False tier_rate=0.13\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 453: task=20 difficulty=expert achieved=True tier_rate=0.26\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 454: task=119 difficulty=expert achieved=True tier_rate=0.37\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 455: task=128 difficulty=expert achieved=False tier_rate=0.32\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 456: task=124 difficulty=expert achieved=True tier_rate=0.42\n",
+ "17:58:55 | INFO | server.services.curriculum | Task 127 GRADUATED (rate=0.72) — scheduling spaced repetition\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 457: task=127 difficulty=expert achieved=True tier_rate=0.51\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 458: task=116 difficulty=expert achieved=False tier_rate=0.43\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 459: task=115 difficulty=expert achieved=False tier_rate=0.37\n",
+ "17:58:55 | INFO | server.services.curriculum | Episode 460: task=109 difficulty=expert achieved=False tier_rate=0.31\n",
+ "17:58:58 | INFO | grpo | [final-grpo] step 33/35 (+213.5s)\n",
+ "17:58:58 | INFO | grpo | [final-grpo] log step=33 {'loss': 0.1873, 'grad_norm': 0.2613, 'learning_rate': 0.0, 'completion_length': 87.125, 'kl': 0.1585, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 154858.0, 'completions/mean_length': 101.8125, 'completions/min_length': 32.0, 'completions/max_length': 194.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 101.8125, 'completions/min_terminated_length': 32.0, 'completions/max_terminated_length': 194.0, 'rewards/env_reward/mean': 0.3894, 'rewards/env_reward/std': 0.4482, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.8731, 'rewards/length_reward/std': 0.1621, 'reward': 2.2625, 'reward_std': 0.5678, 'frac_reward_zero_std': 0.0, 'epoch': 5.5714}\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 461: task=139 difficulty=expert achieved=False tier_rate=0.26\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 462: task=122 difficulty=expert achieved=False tier_rate=0.22\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 463: task=129 difficulty=expert achieved=False tier_rate=0.19\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 464: task=117 difficulty=expert achieved=False tier_rate=0.16\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 465: task=134 difficulty=expert achieved=False tier_rate=0.14\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 466: task=125 difficulty=expert achieved=False tier_rate=0.12\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 467: task=133 difficulty=expert achieved=False tier_rate=0.10\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 468: task=126 difficulty=expert achieved=False tier_rate=0.08\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 469: task=23 difficulty=expert achieved=False tier_rate=0.07\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 470: task=124 difficulty=expert achieved=False tier_rate=0.06\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 471: task=20 difficulty=expert achieved=False tier_rate=0.05\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 472: task=25 difficulty=expert achieved=False tier_rate=0.04\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 473: task=114 difficulty=expert achieved=False tier_rate=0.04\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 474: task=135 difficulty=expert achieved=False tier_rate=0.03\n",
+ "18:02:28 | INFO | server.services.curriculum | Episode 475: task=131 difficulty=expert achieved=False tier_rate=0.03\n",
+ "18:02:30 | INFO | grpo | [final-grpo] step 34/35 (+212.6s)\n",
+ "18:02:30 | INFO | grpo | [final-grpo] log step=34 {'loss': -0.2093, 'grad_norm': 0.2516, 'learning_rate': 0.0, 'completion_length': 94.5, 'kl': 0.1341, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 160004.0, 'completions/mean_length': 80.4375, 'completions/min_length': 26.0, 'completions/max_length': 129.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 80.4375, 'completions/min_terminated_length': 26.0, 'completions/max_terminated_length': 129.0, 'rewards/env_reward/mean': 0.2364, 'rewards/env_reward/std': 0.2318, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.9459, 'rewards/length_reward/std': 0.0736, 'reward': 2.1822, 'reward_std': 0.2709, 'frac_reward_zero_std': 0.0, 'epoch': 5.7619}\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 476: task=122 difficulty=expert achieved=False tier_rate=0.02\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 477: task=25 difficulty=expert achieved=False tier_rate=0.02\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 478: task=131 difficulty=expert achieved=False tier_rate=0.02\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 479: task=129 difficulty=expert achieved=False tier_rate=0.01\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 480: task=115 difficulty=expert achieved=False tier_rate=0.01\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 481: task=120 difficulty=expert achieved=False tier_rate=0.01\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 482: task=126 difficulty=expert achieved=False tier_rate=0.01\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 483: task=117 difficulty=expert achieved=False tier_rate=0.01\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 484: task=128 difficulty=expert achieved=False tier_rate=0.01\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 485: task=134 difficulty=expert achieved=False tier_rate=0.01\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 486: task=23 difficulty=expert achieved=False tier_rate=0.00\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 487: task=123 difficulty=expert achieved=False tier_rate=0.00\n",
+ "18:07:03 | INFO | server.services.curriculum | Task 20 UN-GRADUATED (rate=0.57) — resetting to active\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 488: task=20 difficulty=expert achieved=False tier_rate=0.00\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 489: task=116 difficulty=expert achieved=False tier_rate=0.00\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 490: task=133 difficulty=expert achieved=False tier_rate=0.00\n",
+ "18:07:03 | INFO | server.services.curriculum | Episode 491: task=125 difficulty=expert achieved=False tier_rate=0.00\n",
+ "18:07:06 | INFO | grpo | [final-grpo] step 35/35 (+275.5s)\n",
+ "18:07:06 | INFO | grpo | [final-grpo] log step=35 {'loss': -0.0615, 'grad_norm': 0.2404, 'learning_rate': 0.0, 'completion_length': 74.375, 'kl': 0.126, 'clip_ratio/low_mean': 0.0, 'clip_ratio/low_min': 0.0, 'clip_ratio/high_mean': 0.0, 'clip_ratio/high_max': 0.0, 'clip_ratio/region_mean': 0.0, 'num_tokens': 165202.0, 'completions/mean_length': 83.875, 'completions/min_length': 30.0, 'completions/max_length': 249.0, 'completions/clipped_ratio': 0.0, 'completions/mean_terminated_length': 83.875, 'completions/min_terminated_length': 30.0, 'completions/max_terminated_length': 249.0, 'rewards/env_reward/mean': 0.1922, 'rewards/env_reward/std': 0.1868, 'rewards/format_reward/mean': 1.0, 'rewards/format_reward/std': 0.0, 'rewards/length_reward/mean': 0.932, 'rewards/length_reward/std': 0.123, 'reward': 2.1242, 'reward_std': 0.2548, 'frac_reward_zero_std': 0.0, 'epoch': 5.9524}\n",
+ "18:07:07 | INFO | grpo | [final-grpo] log step=35 {'train_runtime': 6855.0451, 'train_samples_per_second': 0.082, 'train_steps_per_second': 0.005, 'total_flos': 0.0, 'train_loss': 0.0819, 'epoch': 5.9524}\n",
+ "18:07:07 | INFO | grpo | [final-grpo] train_end | global_step=35 elapsed=6855.0s\n",
+ "18:07:08 | INFO | grpo | Final curriculum stats: {'episode_count': 491, 'tier': 'expert', 'tier_episodes': 474, 'tier_success_rate': 0.002, 'graduated_tasks': [110, 119, 127], 'weak_spots': [18, 19, 20, 21, 22, 23, 109, 111, 113, 114, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125, 126, 24, 25, 128, 129, 131, 133, 134, 135, 139], 'skill_profile': {37: 1.0, 33: 0.0, 30: 1.0, 40: 1.0, 39: 0.0, 27: 0.0, 5: 1.0, 31: 1.0, 36: 1.0, 42: 1.0, 32: 1.0, 38: 1.0, 1: 1.0, 35: 1.0, 43: 1.0, 34: 1.0, 44: 1.0, 29: 1.0, 3: 1.0, 0: 1.0, 4: 1.0, 28: 1.0, 41: 1.0, 100: 0.0, 103: 0.0, 97: 0.0, 90: 0.0, 95: 0.0, 91: 0.0, 101: 0.0, 104: 0.0, 105: 0.0, 17: 0.0, 107: 0.0, 108: 0.0, 92: 0.0, 106: 0.0, 111: 0.22, 119: 0.89, 129: 0.0, 121: 0.05, 23: 0.18, 139: 0.27, 124: 0.16, 22: 0.42, 114: 0.0, 134: 0.11, 133: 0.0, 21: 0.39, 123: 0.1, 120: 0.0, 116: 0.0, 122: 0.0, 135: 0.0, 24: 0.0, 128: 0.0, 18: 0.0, 25: 0.0, 127: 0.72, 131: 0.06, 126: 0.0, 110: 0.87, 118: 0.0, 125: 0.0, 113: 0.0, 109: 0.04, 115: 0.0, 20: 0.57, 19: 0.0, 117: 0.0}, 'spaced_rep_due': [110, 119, 127], 'avg_reward_last_10': 0.176}\n",
+ "18:07:09 | INFO | grpo | Saved GRPO adapter to /content/out/grpo_adapter\n"
+ ]
+ }
+ ],
+ "source": [
+ "import json\n",
+ "from dataclasses import asdict\n",
+ "\n",
+ "\n",
+ "class CheckpointManager:\n",
+ " \"\"\"Find the latest `checkpoint-N/` under `root` for safe resume.\"\"\"\n",
+ "\n",
+ " def __init__(self, root: Path) -> None:\n",
+ " self.root = root\n",
+ "\n",
+ " def latest(self) -> str | None:\n",
+ " if not self.root.exists():\n",
+ " return None\n",
+ " ckpts = sorted(\n",
+ " (d for d in self.root.glob(\"checkpoint-*\") if d.is_dir()),\n",
+ " key=lambda d: int(d.name.split(\"-\")[-1]),\n",
+ " )\n",
+ " return str(ckpts[-1]) if ckpts else None\n",
+ "\n",
+ "\n",
+ "FINAL_RUN_DIR = OUT_DIR / \"final_grpo\"\n",
+ "ckpt_mgr = CheckpointManager(FINAL_RUN_DIR)\n",
+ "resume_from = ckpt_mgr.latest()\n",
+ "\n",
+ "# Make the final-run cell re-runnable after a kernel restart: if `best_cfg` is\n",
+ "# not in the namespace (Optuna didn't run this session), recover it from the\n",
+ "# persisted optuna_best.json; if that's missing too, fall back to TRAIN\n",
+ "# defaults so the run still launches on the sane baseline hparams.\n",
+ "try:\n",
+ " best_cfg\n",
+ "except NameError:\n",
+ " _best_path = OUT_DIR / \"optuna_best.json\"\n",
+ " if _best_path.exists():\n",
+ " _resolved = json.loads(_best_path.read_text())[\"resolved_config\"]\n",
+ " best_cfg = replace(TRAIN, **{\n",
+ " k: v for k, v in _resolved.items()\n",
+ " if k in TrainingConfig.__dataclass_fields__\n",
+ " })\n",
+ " log.info(\"Recovered best_cfg from %s\", _best_path)\n",
+ " else:\n",
+ " best_cfg = TRAIN\n",
+ " log.info(\"No Optuna results found; using TRAIN defaults for best_cfg\")\n",
+ "\n",
+ "# Master curriculum for the final run — single instance that progresses\n",
+ "# through tiers as the agent demonstrates mastery. Lives for the whole run.\n",
+ "FINAL_CURRICULUM = Curriculum(tasks_dir=_tasks_dir)\n",
+ "# Superset dataset (all tiers). The CurriculumTierSampler (wired in\n",
+ "# build_trainer) filters indices by FINAL_CURRICULUM.current_difficulty\n",
+ "# on every yield, so promotion during training takes effect on the very\n",
+ "# next batch instead of being frozen out by an up-front materialisation.\n",
+ "FINAL_TRAIN_DS = make_full_curriculum_dataset(_tasks_dir)\n",
+ "_final_num_samples = int(\n",
+ " PIPE.final_max_steps\n",
+ " * best_cfg.per_device_train_batch_size\n",
+ " * best_cfg.gradient_accumulation_steps\n",
+ " * 1.2\n",
+ ")\n",
+ "final_model, final_tok = load_policy(MODEL, trainable=True)\n",
+ "\n",
+ "final_env_r, final_fmt_r, final_len_r = build_reward_funcs(\n",
+ " ENV_CLIENT, TASK_MAP, FINAL_CURRICULUM,\n",
+ " model=final_model, tokenizer=final_tok,\n",
+ ")\n",
+ "\n",
+ "final_trainer = build_trainer(\n",
+ " final_model, final_tok,\n",
+ " train_ds=FINAL_TRAIN_DS,\n",
+ " eval_ds=VAL_DS,\n",
+ " reward_funcs=(final_env_r, final_fmt_r, final_len_r),\n",
+ " cfg=best_cfg,\n",
+ " output_dir=str(FINAL_RUN_DIR),\n",
+ " run_name=\"final-grpo\",\n",
+ " use_fp16=RT.use_fp16, use_bf16=RT.use_bf16,\n",
+ " max_steps=PIPE.final_max_steps,\n",
+ " save_strategy=\"steps\",\n",
+ " # Skip TRL's mid-training eval — its GRPO prediction_step reshapes\n",
+ " # rewards as (-1, num_generations) which blows up when eval generates\n",
+ " # a different completion count per prompt than training. We do full\n",
+ " # env-reward eval outside TRL via evaluate_single_step anyway.\n",
+ " eval_strategy=\"no\",\n",
+ " curriculum=FINAL_CURRICULUM,\n",
+ " num_samples=_final_num_samples,\n",
+ ")\n",
+ "\n",
+ "if resume_from:\n",
+ " log.info(\"Resuming GRPO from %s\", resume_from)\n",
+ "final_trainer.train(resume_from_checkpoint=resume_from)\n",
+ "\n",
+ "# Log final curriculum stats so we can see tier progression / mastery counts\n",
+ "final_stats = FINAL_CURRICULUM.get_stats()\n",
+ "log.info(\"Final curriculum stats: %s\", final_stats)\n",
+ "\n",
+ "# Persist the final LoRA adapter locally before anything else touches VRAM\n",
+ "adapter_local = OUT_DIR / \"grpo_adapter\"\n",
+ "final_trainer.model.save_pretrained(str(adapter_local))\n",
+ "final_tok.save_pretrained(str(adapter_local))\n",
+ "log.info(\"Saved GRPO adapter to %s\", adapter_local)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "09e8f092",
+ "metadata": {
+ "id": "09e8f092"
+ },
+ "source": [
+ "## 15 · Multi-step evaluation harness\n",
+ "\n",
+ "Training was single-step for TRL compatibility; *evaluation* runs full episodes so we can measure:\n",
+ "\n",
+ "- per-tier task success\n",
+ "- hints used per solved task\n",
+ "- recovery rate after chaos injection\n",
+ "- drift repair rate\n",
+ "- steps to solve\n",
+ "- rollback count (safety)\n",
+ "- generalization gap (reserve vs train-held-out)\n",
+ "\n",
+ "The pattern mirrors [`scripts/grpo_train.py`](https://github.com/UdayKiranPadhy/aws-rl-env/blob/master/scripts/grpo_train.py)'s `run_single_rollout`, but uses `GrpoPool` for 8-way concurrent rollouts so a 100-task eval finishes in ~7 minutes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "d18fb6e4",
+ "metadata": {
+ "id": "d18fb6e4",
+ "outputId": "7503d657-b50e-4704-a31a-08a47c0f3c77",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "18:07:19 | INFO | grpo | run_episode defined.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import asyncio\n",
+ "from collections import defaultdict\n",
+ "from models import AwsRlAction\n",
+ "from client import AwsRlEnv\n",
+ "from scripts.grpo_pool import GrpoPool\n",
+ "\n",
+ "\n",
+ "# Multi-step eval uses a richer prompt that includes command history.\n",
+ "# Separate from the single-step SYSTEM_PROMPT to avoid shadowing it.\n",
+ "EVAL_SYSTEM_PROMPT = (\n",
+ " \"You are an expert AWS SRE agent. You operate a simulated AWS cloud by \"\n",
+ " \"emitting one AWS CLI command at a time. You will see the task description \"\n",
+ " \"and the most recent command output.\\n\\n\"\n",
+ " \"First reason about your next move inside a ... block: use \"\n",
+ " \"the latest OUTPUT to decide what to do next, pick the AWS service and \"\n",
+ " \"subcommand, and list the arguments you need. Keep the reasoning brief.\\n\\n\"\n",
+ " \"After , on a NEW LINE, output EXACTLY ONE AWS CLI command starting \"\n",
+ " \"with 'aws '. The command line must contain only the command \\u2014 no \"\n",
+ " \"markdown, no backticks, no quotes around it, and no trailing commentary.\"\n",
+ ")\n",
+ "\n",
+ "\n",
+ "def build_multi_step_prompt(tokenizer, task: Task,\n",
+ " history: list[tuple[str, str]]) -> str:\n",
+ " \"\"\"Assemble chat-style prompt including the last few (cmd, output) turns.\"\"\"\n",
+ " messages = [\n",
+ " {\"role\": \"system\", \"content\": EVAL_SYSTEM_PROMPT},\n",
+ " {\"role\": \"user\", \"content\": f\"TASK: {task.description}\"},\n",
+ " ]\n",
+ " for cmd, out in history[-4:]: # last 4 turns fit in 512 tokens\n",
+ " messages.append({\"role\": \"assistant\", \"content\": cmd})\n",
+ " messages.append({\"role\": \"user\", \"content\": f\"OUTPUT:\\n{out[:400]}\"})\n",
+ " return tokenizer.apply_chat_template(\n",
+ " messages, tokenize=False, add_generation_prompt=True,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "@dataclass\n",
+ "class EpisodeResult:\n",
+ " task_id: int\n",
+ " tier: str\n",
+ " is_drift: bool\n",
+ " achieved: bool\n",
+ " terminal_reward: float\n",
+ " steps_taken: int\n",
+ " hints_used: int\n",
+ " chaos_occurred: bool\n",
+ " command_failures: int\n",
+ "\n",
+ "\n",
+ "async def run_episode(env: AwsRlEnv, model, tokenizer,\n",
+ " task: Task, drift_ids: set[int],\n",
+ " max_steps: int = 15,\n",
+ " # Bumped 96 → 512 so per-step generation has room\n",
+ " # for a … block plus the command.\n",
+ " max_new_tokens: int = 512) -> EpisodeResult:\n",
+ " \"\"\"Roll one episode against one env session. Sampling temperature is low\n",
+ " to reflect deployment behaviour rather than training-time exploration.\"\"\"\n",
+ " device = next(model.parameters()).device\n",
+ " res = await env.reset(task=task)\n",
+ " history: list[tuple[str, str]] = []\n",
+ " steps_taken = 0\n",
+ " command_failures = 0\n",
+ " terminal_reward = 0.0\n",
+ " achieved = False\n",
+ "\n",
+ " for _ in range(max_steps):\n",
+ " steps_taken += 1\n",
+ " prompt = build_multi_step_prompt(tokenizer, task, history)\n",
+ " inputs = tokenizer(prompt, return_tensors=\"pt\").to(device)\n",
+ " with torch.inference_mode():\n",
+ " ids = model.generate(\n",
+ " **inputs,\n",
+ " max_new_tokens=max_new_tokens,\n",
+ " do_sample=True,\n",
+ " temperature=0.4,\n",
+ " top_p=0.9,\n",
+ " pad_token_id=tokenizer.eos_token_id,\n",
+ " )\n",
+ " text = tokenizer.decode(\n",
+ " ids[0, inputs.input_ids.shape[1]:], skip_special_tokens=True,\n",
+ " )\n",
+ " cmd = extract_aws_command(text)\n",
+ " res = await env.step(AwsRlAction(command=cmd))\n",
+ " terminal_reward = float(res.reward)\n",
+ " obs = res.observation\n",
+ " if not obs.command_success:\n",
+ " command_failures += 1\n",
+ " history.append((cmd, obs.command_output or \"\"))\n",
+ " if obs.task_achieved:\n",
+ " achieved = True\n",
+ " if res.done:\n",
+ " break\n",
+ "\n",
+ " # One final /state poll for chaos flag — TrackerState doesn't expose\n",
+ " # rollback counts, so we skip that metric rather than report zeros.\n",
+ " try:\n",
+ " state = await env.state()\n",
+ " chaos = bool(getattr(state, \"chaos_occurred\", False))\n",
+ " except Exception:\n",
+ " chaos = False\n",
+ "\n",
+ " return EpisodeResult(\n",
+ " task_id=int(task.task_id),\n",
+ " tier=task.difficulty.value,\n",
+ " is_drift=int(task.task_id) in drift_ids,\n",
+ " achieved=achieved,\n",
+ " terminal_reward=terminal_reward,\n",
+ " steps_taken=steps_taken,\n",
+ " hints_used=int(getattr(res.observation, \"hints_used\", 0) or 0),\n",
+ " chaos_occurred=chaos,\n",
+ " command_failures=command_failures,\n",
+ " )\n",
+ "\n",
+ "\n",
+ "log.info(\"run_episode defined.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "4f4dacfe",
+ "metadata": {
+ "id": "4f4dacfe",
+ "outputId": "b702b6ec-93bb-4d62-cdaf-d04b6e9f872c",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "18:26:40 | INFO | grpo | evaluate_multi_step defined.\n"
+ ]
+ }
+ ],
+ "source": [
+ "@dataclass\n",
+ "class RichMetrics:\n",
+ " \"\"\"All the metrics the hackathon judges care about.\"\"\"\n",
+ " success_by_tier: dict\n",
+ " reward_by_tier: dict\n",
+ " overall_success_rate: float\n",
+ " overall_reward_mean: float\n",
+ " hints_per_solved: float\n",
+ " recovery_rate: float\n",
+ " drift_repair_rate: float\n",
+ " steps_to_solve: float\n",
+ " destructive_fail_rate: float\n",
+ " n_episodes: int\n",
+ "\n",
+ " def as_dict(self) -> dict:\n",
+ " return asdict(self)\n",
+ "\n",
+ "\n",
+ "def summarize_episodes(results: list[EpisodeResult]) -> RichMetrics:\n",
+ " \"\"\"Aggregate per-tier and overall stats from a list of EpisodeResults.\n",
+ "\n",
+ " Drift detection uses the per-result `is_drift` flag (set from DRIFT_TASK_IDS)\n",
+ " rather than a tier string — drift tasks live inside the EXPERT tier files.\n",
+ " \"\"\"\n",
+ " by_tier: dict[str, list[EpisodeResult]] = defaultdict(list)\n",
+ " for r in results:\n",
+ " by_tier[r.tier].append(r)\n",
+ "\n",
+ " success_by_tier = {tier: sum(r.achieved for r in xs) / max(1, len(xs))\n",
+ " for tier, xs in by_tier.items()}\n",
+ " reward_by_tier = {tier: (sum(r.terminal_reward for r in xs) / max(1, len(xs)))\n",
+ " for tier, xs in by_tier.items()}\n",
+ "\n",
+ " solved = [r for r in results if r.achieved]\n",
+ " chaos_episodes = [r for r in results if r.chaos_occurred]\n",
+ " drift_episodes = [r for r in results if r.is_drift]\n",
+ "\n",
+ " return RichMetrics(\n",
+ " success_by_tier=success_by_tier,\n",
+ " reward_by_tier=reward_by_tier,\n",
+ " overall_success_rate=len(solved) / max(1, len(results)),\n",
+ " overall_reward_mean=sum(r.terminal_reward for r in results) / max(1, len(results)),\n",
+ " hints_per_solved=(sum(r.hints_used for r in solved) / len(solved)) if solved else 0.0,\n",
+ " recovery_rate=(sum(r.achieved for r in chaos_episodes) / len(chaos_episodes))\n",
+ " if chaos_episodes else 0.0,\n",
+ " drift_repair_rate=(sum(r.achieved for r in drift_episodes) / len(drift_episodes))\n",
+ " if drift_episodes else 0.0,\n",
+ " steps_to_solve=(sum(r.steps_taken for r in solved) / len(solved)) if solved else 0.0,\n",
+ " destructive_fail_rate=sum(r.command_failures > 0 for r in results) / max(1, len(results)),\n",
+ " n_episodes=len(results),\n",
+ " )\n",
+ "\n",
+ "\n",
+ "async def evaluate_multi_step(base_url: str, task_ids: list[int],\n",
+ " task_map: dict[int, Task],\n",
+ " drift_ids: set[int],\n",
+ " model, tokenizer,\n",
+ " pool_size: int = 8,\n",
+ " max_steps: int = 15) -> RichMetrics:\n",
+ " \"\"\"Run one episode per task_id across `pool_size` concurrent env sessions.\n",
+ "\n",
+ " Opens a fresh GrpoPool per chunk. Reusing a single pool across chunks\n",
+ " fails because the env server / Cloudflare tunnel closes the WebSocket\n",
+ " (code 1000) once an episode terminates or the connection idles during\n",
+ " a long generate() call; the next chunk's reset() then raises\n",
+ " ConnectionClosedOK on a dead socket. Per-chunk pools cost N WebSocket\n",
+ " handshakes per chunk but keep the eval correct.\n",
+ " \"\"\"\n",
+ " results: list[EpisodeResult] = []\n",
+ " chunks = [task_ids[i:i + pool_size]\n",
+ " for i in range(0, len(task_ids), pool_size)]\n",
+ " log.info(\"multi-step eval: %d tasks in %d chunks (pool_size=%d)\",\n",
+ " len(task_ids), len(chunks), pool_size)\n",
+ " for ci, chunk in enumerate(chunks, 1):\n",
+ " async with GrpoPool(base_url=base_url, size=len(chunk)) as pool:\n",
+ " coros = [run_episode(env, model, tokenizer, task_map[tid],\n",
+ " drift_ids, max_steps=max_steps)\n",
+ " for env, tid in zip(pool.envs, chunk)]\n",
+ " chunk_results = await asyncio.gather(*coros, return_exceptions=True)\n",
+ " ok = sum(1 for r in chunk_results if isinstance(r, EpisodeResult))\n",
+ " log.info(\"chunk %d/%d: %d/%d episodes ok\",\n",
+ " ci, len(chunks), ok, len(chunk))\n",
+ " for r in chunk_results:\n",
+ " if isinstance(r, EpisodeResult):\n",
+ " results.append(r)\n",
+ " else:\n",
+ " log.warning(\"[eval] episode raised %s: %s\",\n",
+ " type(r).__name__, r)\n",
+ " if not results:\n",
+ " log.error(\"multi-step eval produced 0 results — env connection problem?\")\n",
+ " return summarize_episodes(results)\n",
+ "\n",
+ "\n",
+ "def select_eval_task_ids(reserve_ds, drift_ids: set[int], cap: int) -> list[int]:\n",
+ " \"\"\"Task ids for the multi-step eval: reserve split + every drift task.\"\"\"\n",
+ " tids = [int(r[\"task_id\"]) for r in reserve_ds][:cap] if reserve_ds else []\n",
+ " for did in drift_ids:\n",
+ " if did not in tids:\n",
+ " tids.append(did)\n",
+ " return tids\n",
+ "\n",
+ "\n",
+ "log.info(\"evaluate_multi_step defined.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e40d1ce7",
+ "metadata": {
+ "id": "e40d1ce7"
+ },
+ "source": [
+ "## 16 · Before / after multi-step evaluation\n",
+ "\n",
+ "Runs the rich multi-step evaluator once with the **SFT-only** model (baseline) and once with the **final GRPO** model, then prints a delta table. Per-tier and overall comparisons are plotted by the matplotlib cell below.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "69073028",
+ "metadata": {
+ "id": "69073028",
+ "outputId": "673ea74b-90bc-4de1-84c8-2c38bafe1779",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "18:26:42 | INFO | grpo | Multi-step eval on 109 tasks (9 drift).\n",
+ "18:26:43 | INFO | grpo | Closed ENV_CLIENT; waiting 3s for server to release 8 MiniStack slots.\n",
+ "18:26:46 | INFO | grpo | Evaluating SFT baseline (multi-step)...\n",
+ "==((====))== Unsloth 2026.4.8: Fast Qwen2 patching. Transformers: 4.57.6.\n",
+ " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n",
+ "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n",
+ "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.35. FA2 = False]\n",
+ " \"-____-\" Free license: http://github.com/unslothai/unsloth\n",
+ "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
+ "18:27:05 | INFO | grpo | multi-step eval: 109 tasks in 14 chunks (pool_size=8)\n",
+ "18:27:05 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:27:05 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:27:06 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:27:50 | INFO | grpo | chunk 1/14: 8/8 episodes ok\n",
+ "18:27:50 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:27:50 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:27:51 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:29:02 | INFO | grpo | chunk 2/14: 8/8 episodes ok\n",
+ "18:29:02 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:29:02 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:29:03 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:30:40 | INFO | grpo | chunk 3/14: 8/8 episodes ok\n",
+ "18:30:40 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:30:40 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:30:41 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:31:13 | INFO | grpo | chunk 4/14: 8/8 episodes ok\n",
+ "18:31:13 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:31:13 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:31:14 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:31:39 | INFO | grpo | chunk 5/14: 8/8 episodes ok\n",
+ "18:31:39 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:31:39 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:31:40 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:32:33 | INFO | grpo | chunk 6/14: 8/8 episodes ok\n",
+ "18:32:33 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:32:33 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:32:34 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:33:21 | INFO | grpo | chunk 7/14: 5/8 episodes ok\n",
+ "18:33:21 | WARNING | grpo | [eval] episode raised ConnectionClosedError: received 1011 (internal error) keepalive ping timeout; then sent 1011 (internal error) keepalive ping timeout\n",
+ "18:33:21 | WARNING | grpo | [eval] episode raised ConnectionClosedError: received 1011 (internal error) keepalive ping timeout; then sent 1011 (internal error) keepalive ping timeout\n",
+ "18:33:21 | WARNING | grpo | [eval] episode raised ConnectionClosedError: received 1011 (internal error) keepalive ping timeout; then sent 1011 (internal error) keepalive ping timeout\n",
+ "18:33:21 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:33:21 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:33:22 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:34:27 | INFO | grpo | chunk 8/14: 8/8 episodes ok\n",
+ "18:34:27 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:34:27 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:34:28 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:35:18 | INFO | grpo | chunk 9/14: 8/8 episodes ok\n",
+ "18:35:18 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:35:18 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:35:19 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:36:00 | INFO | grpo | chunk 10/14: 8/8 episodes ok\n",
+ "18:36:00 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:36:00 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:36:01 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:36:31 | INFO | grpo | chunk 11/14: 8/8 episodes ok\n",
+ "18:36:31 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:36:31 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:36:32 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:37:07 | INFO | grpo | chunk 12/14: 8/8 episodes ok\n",
+ "18:37:07 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:37:07 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:37:08 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:39:41 | INFO | grpo | chunk 13/14: 8/8 episodes ok\n",
+ "18:39:41 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:39:41 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:39:42 | INFO | scripts.grpo_pool | GrpoPool connected: 5 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:43:13 | INFO | grpo | chunk 14/14: 5/5 episodes ok\n",
+ "18:43:15 | INFO | grpo | Evaluating GRPO-trained model (multi-step)...\n",
+ "18:43:15 | INFO | grpo | multi-step eval: 109 tasks in 14 chunks (pool_size=8)\n",
+ "18:43:15 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:43:15 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:43:17 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:44:18 | INFO | grpo | chunk 1/14: 8/8 episodes ok\n",
+ "18:44:18 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:44:18 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:44:19 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:46:05 | INFO | grpo | chunk 2/14: 8/8 episodes ok\n",
+ "18:46:05 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:46:05 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:46:06 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:46:56 | INFO | grpo | chunk 3/14: 8/8 episodes ok\n",
+ "18:46:56 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:46:56 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:46:57 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:47:48 | INFO | grpo | chunk 4/14: 8/8 episodes ok\n",
+ "18:47:48 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:47:48 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:47:49 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:48:12 | INFO | grpo | chunk 5/14: 8/8 episodes ok\n",
+ "18:48:12 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:48:12 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:48:13 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:49:06 | INFO | grpo | chunk 6/14: 8/8 episodes ok\n",
+ "18:49:06 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:49:06 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:49:07 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:49:48 | INFO | grpo | chunk 7/14: 8/8 episodes ok\n",
+ "18:49:48 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:49:48 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:49:49 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:50:54 | INFO | grpo | chunk 8/14: 8/8 episodes ok\n",
+ "18:50:54 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:50:54 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:50:55 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:51:55 | INFO | grpo | chunk 9/14: 8/8 episodes ok\n",
+ "18:51:55 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:51:55 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:51:56 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:52:41 | INFO | grpo | chunk 10/14: 8/8 episodes ok\n",
+ "18:52:41 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:52:41 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:52:43 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:53:11 | INFO | grpo | chunk 11/14: 8/8 episodes ok\n",
+ "18:53:11 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:53:11 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:53:12 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:53:50 | INFO | grpo | chunk 12/14: 8/8 episodes ok\n",
+ "18:53:50 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:53:50 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:53:51 | INFO | scripts.grpo_pool | GrpoPool connected: 8 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:56:21 | INFO | grpo | chunk 13/14: 8/8 episodes ok\n",
+ "18:56:21 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:56:21 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:56:22 | INFO | scripts.grpo_pool | GrpoPool connected: 5 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "18:58:59 | INFO | grpo | chunk 14/14: 5/5 episodes ok\n",
+ "18:58:59 | INFO | grpo | SFT vs GRPO deltas: {}\n",
+ "\n",
+ "| Metric | SFT baseline | GRPO | Delta |\n",
+ "|----------------------------|-------------:|------------:|-------:|\n",
+ "| overall_success_rate | 0.868 | 0.862 | -0.006 |\n",
+ "| overall_reward_mean | 0.883 | 0.877 | -0.006 |\n",
+ "| hints_per_solved | 0.000 | 0.000 | +0.000 |\n",
+ "| recovery_rate | 0.333 | 0.000 | -0.333 |\n",
+ "| drift_repair_rate | 0.222 | 0.222 | +0.000 |\n",
+ "| steps_to_solve | 1.446 | 1.553 | +0.108 |\n",
+ "| destructive_fail_rate | 0.151 | 0.147 | -0.004 |\n",
+ "| success[beginner] | 0.962 | 1.000 | +0.038 |\n",
+ "| success[expert] | 0.222 | 0.222 | +0.000 |\n",
+ "| success[intermediate] | 0.810 | 0.870 | +0.060 |\n",
+ "| success[warmup] | 0.960 | 0.902 | -0.058 |\n"
+ ]
+ }
+ ],
+ "source": [
+ "def _flatten_metrics(m: RichMetrics, prefix: str) -> dict:\n",
+ " out = {}\n",
+ " for k, v in m.as_dict().items():\n",
+ " if isinstance(v, dict):\n",
+ " for tier, val in v.items():\n",
+ " out[f\"{prefix}/{k}/{tier}\"] = val\n",
+ " else:\n",
+ " out[f\"{prefix}/{k}\"] = v\n",
+ " return out\n",
+ "\n",
+ "\n",
+ "def _delta_metrics(before: RichMetrics, after: RichMetrics) -> dict:\n",
+ " b, a = before.as_dict(), after.as_dict()\n",
+ " delta: dict[str, float] = {}\n",
+ " for k in a:\n",
+ " if isinstance(a[k], dict):\n",
+ " for tier, v in a[k].items():\n",
+ " bv = b.get(k, {}).get(tier, 0.0)\n",
+ " delta[f\"eval/delta_{k}/{tier}\"] = v - bv\n",
+ " elif isinstance(a[k], (int, float)):\n",
+ " delta[f\"eval/delta_{k}\"] = a[k] - b[k]\n",
+ " return delta\n",
+ "\n",
+ "\n",
+ "EVAL_TASK_IDS = select_eval_task_ids(RESERVE_DS, DRIFT_TASK_IDS, cap=PIPE.eval_reserve_cap)\n",
+ "log.info(\"Multi-step eval on %d tasks (%d drift).\",\n",
+ " len(EVAL_TASK_IDS),\n",
+ " sum(1 for t in EVAL_TASK_IDS if t in DRIFT_TASK_IDS))\n",
+ "\n",
+ "# Release ENV_CLIENT's 8 WebSocket sessions so the server's MiniStack\n",
+ "# slots are free for GrpoPool. Without this, every eval episode dies\n",
+ "# instantly with ConnectionClosedOK / CAPACITY_REACHED because all 8\n",
+ "# slots are still held by the training reward client.\n",
+ "try:\n",
+ " ENV_CLIENT.close()\n",
+ " log.info(\"Closed ENV_CLIENT; waiting 3s for server to release 8 MiniStack slots.\")\n",
+ " await asyncio.sleep(3)\n",
+ "except Exception as e:\n",
+ " log.warning(\"ENV_CLIENT.close() raised %s: %s\", type(e).__name__, e)\n",
+ "\n",
+ "# --- SFT baseline ---\n",
+ "log.info(\"Evaluating SFT baseline (multi-step)...\")\n",
+ "sft_model, sft_tok = load_policy(MODEL, trainable=False)\n",
+ "sft_metrics = await evaluate_multi_step(\n",
+ " ENV_BASE_URL, EVAL_TASK_IDS, TASK_MAP, DRIFT_TASK_IDS,\n",
+ " sft_model, sft_tok, pool_size=PIPE.env_pool_size,\n",
+ ")\n",
+ "free_model(sft_model); del sft_tok\n",
+ "gc.collect(); torch.cuda.empty_cache()\n",
+ "(OUT_DIR / \"baseline_multi_step.json\").write_text(json.dumps(sft_metrics.as_dict(), indent=2))\n",
+ "\n",
+ "# --- GRPO after ---\n",
+ "log.info(\"Evaluating GRPO-trained model (multi-step)...\")\n",
+ "from unsloth import FastLanguageModel\n",
+ "if \"final_trainer\" not in globals() or \"final_tok\" not in globals():\n",
+ " from peft import PeftModel\n",
+ " _grpo_dir = OUT_DIR / \"grpo_adapter\"\n",
+ " log.info(\"final_trainer not in globals; reloading GRPO adapter from %s...\", _grpo_dir)\n",
+ " _base, final_tok = FastLanguageModel.from_pretrained(\n",
+ " model_name=MODEL.base_model,\n",
+ " max_seq_length=MODEL.max_seq_length,\n",
+ " load_in_4bit=True,\n",
+ " )\n",
+ " _grpo_model = PeftModel.from_pretrained(_base, str(_grpo_dir), is_trainable=False)\n",
+ " class _TrainerShim:\n",
+ " pass\n",
+ " final_trainer = _TrainerShim()\n",
+ " final_trainer.model = _grpo_model\n",
+ "FastLanguageModel.for_inference(final_trainer.model)\n",
+ "grpo_metrics = await evaluate_multi_step(\n",
+ " ENV_BASE_URL, EVAL_TASK_IDS, TASK_MAP, DRIFT_TASK_IDS,\n",
+ " final_trainer.model, final_tok, pool_size=PIPE.env_pool_size,\n",
+ ")\n",
+ "(OUT_DIR / \"grpo_multi_step.json\").write_text(json.dumps(grpo_metrics.as_dict(), indent=2))\n",
+ "\n",
+ "deltas = _delta_metrics(sft_metrics, grpo_metrics)\n",
+ "log.info(\"SFT vs GRPO deltas: %s\",\n",
+ " {k: round(v, 4) for k, v in deltas.items()\n",
+ " if isinstance(v, (int, float)) and \"/\" not in k})\n",
+ "\n",
+ "def _render_row(name, b, a):\n",
+ " return f\"| {name:<26} | {b:>12.3f} | {a:>12.3f} | {a - b:+.3f} |\"\n",
+ "\n",
+ "print(\"\\n| Metric | SFT baseline | GRPO | Delta |\")\n",
+ "print(\"|----------------------------|-------------:|------------:|-------:|\")\n",
+ "print(_render_row(\"overall_success_rate\", sft_metrics.overall_success_rate, grpo_metrics.overall_success_rate))\n",
+ "print(_render_row(\"overall_reward_mean\", sft_metrics.overall_reward_mean, grpo_metrics.overall_reward_mean))\n",
+ "print(_render_row(\"hints_per_solved\", sft_metrics.hints_per_solved, grpo_metrics.hints_per_solved))\n",
+ "print(_render_row(\"recovery_rate\", sft_metrics.recovery_rate, grpo_metrics.recovery_rate))\n",
+ "print(_render_row(\"drift_repair_rate\", sft_metrics.drift_repair_rate, grpo_metrics.drift_repair_rate))\n",
+ "print(_render_row(\"steps_to_solve\", sft_metrics.steps_to_solve, grpo_metrics.steps_to_solve))\n",
+ "print(_render_row(\"destructive_fail_rate\", sft_metrics.destructive_fail_rate, grpo_metrics.destructive_fail_rate))\n",
+ "\n",
+ "for tier in sorted(set(sft_metrics.success_by_tier) | set(grpo_metrics.success_by_tier)):\n",
+ " b = sft_metrics.success_by_tier.get(tier, 0.0)\n",
+ " a = grpo_metrics.success_by_tier.get(tier, 0.0)\n",
+ " print(_render_row(f\"success[{tier}]\", b, a))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "00a1c8e5",
+ "metadata": {
+ "id": "00a1c8e5"
+ },
+ "source": [
+ "## 17 · Qualitative rollouts — one task per tier\n",
+ "\n",
+ "A small set of showable transcripts (task, generated command, env output, reward). Judges love seeing actual agent behaviour, not just numbers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "id": "5c75fb46",
+ "metadata": {
+ "id": "5c75fb46",
+ "outputId": "99f3a5c1-e1d4-4308-c281-8c5f8f69720e",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "18:59:10 | INFO | server.services.curriculum | Loaded 25 warmup tasks total\n",
+ "18:59:10 | INFO | server.services.curriculum | Curriculum initialised — starting at warmup with 25 tasks\n",
+ "18:59:11 | INFO | scripts.grpo_pool | GrpoPool connected: 5 sessions against https://sizzing-aws-rl-env.hf.space\n",
+ "[\n",
+ " {\n",
+ " \"tier\": \"warmup\",\n",
+ " \"task_id\": 0,\n",
+ " \"description\": \"List all S3 buckets in the environment.\",\n",
+ " \"achieved\": false,\n",
+ " \"terminal_reward\": 0.0,\n",
+ " \"steps_taken\": 8\n",
+ " },\n",
+ " {\n",
+ " \"tier\": \"beginner\",\n",
+ " \"task_id\": 6,\n",
+ " \"description\": \"Create an S3 bucket named 'my-test-bucket'.\",\n",
+ " \"achieved\": true,\n",
+ " \"terminal_reward\": 1.0,\n",
+ " \"steps_taken\": 1\n",
+ " },\n",
+ " {\n",
+ " \"tier\": \"intermediate\",\n",
+ " \"task_id\": 11,\n",
+ " \"description\": \"Create an S3 bucket named 'data-pipeline' and upload a file to it.\",\n",
+ " \"achieved\": true,\n",
+ " \"terminal_reward\": 1.0,\n",
+ " \"steps_taken\": 2\n",
+ " },\n",
+ " {\n",
+ " \"tier\": \"advanced\",\n",
+ " \"task_id\": 15,\n",
+ " \"description\": \"Create a Lambda function 'processor' with an IAM execution role, then create an SQS queue 'work-items' and configure it as an event source for the Lambda function.\\n\",\n",
+ " \"achieved\": false,\n",
+ " \"terminal_reward\": 0.02,\n",
+ " \"steps_taken\": 8\n",
+ " },\n",
+ " {\n",
+ " \"tier\": \"expert\",\n",
+ " \"task_id\": 18,\n",
+ " \"description\": \"SRE Incident: A Lambda function 'order-processor' exists but its IAM role is missing the required SQS permissions. The function's event source mapping to the 'incoming-orders' SQS queue is failing. Diagnose the issue, attach the correct SQS policy to the role, and create the event source mapping.\\n\",\n",
+ " \"achieved\": false,\n",
+ " \"terminal_reward\": 0.06,\n",
+ " \"steps_taken\": 8\n",
+ " }\n",
+ "]\n"
+ ]
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "1347"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 30
+ }
+ ],
+ "source": [
+ "async def qualitative_rollouts(model, tokenizer, task_map: dict[int, Task],\n",
+ " drift_ids: set[int],\n",
+ " samples_per_tier: int = 1) -> list[dict]:\n",
+ " \"\"\"Pick one task per difficulty tier and print a full rollout transcript.\"\"\"\n",
+ " per_tier: dict[str, list[Task]] = defaultdict(list)\n",
+ " for t in task_map.values():\n",
+ " per_tier[t.difficulty.value].append(t)\n",
+ " chosen: list[Task] = []\n",
+ " for tier in [\"warmup\", \"beginner\", \"intermediate\", \"advanced\", \"expert\"]:\n",
+ " if per_tier.get(tier):\n",
+ " chosen.extend(per_tier[tier][:samples_per_tier])\n",
+ "\n",
+ " transcripts = []\n",
+ " async with GrpoPool(base_url=ENV_BASE_URL, size=min(len(chosen), PIPE.env_pool_size)) as pool:\n",
+ " for env, task in zip(pool.envs, chosen):\n",
+ " ep = await run_episode(env, model, tokenizer, task, drift_ids, max_steps=8)\n",
+ " transcripts.append({\n",
+ " \"tier\": task.difficulty.value,\n",
+ " \"task_id\": int(task.task_id),\n",
+ " \"description\": task.description,\n",
+ " \"achieved\": ep.achieved,\n",
+ " \"terminal_reward\": ep.terminal_reward,\n",
+ " \"steps_taken\": ep.steps_taken,\n",
+ " })\n",
+ " return transcripts\n",
+ "\n",
+ "\n",
+ "# Reload the GRPO adapter from disk if final_trainer was purged by an\n",
+ "# earlier VRAM reclaim (e.g. the Optuna cell). Same fallback as the\n",
+ "# multi-step eval cell: the final-run cell persists the adapter to\n",
+ "# OUT_DIR/\"grpo_adapter\", so that's the source of truth.\n",
+ "if \"final_trainer\" not in globals() or \"final_tok\" not in globals():\n",
+ " from unsloth import FastLanguageModel\n",
+ " from peft import PeftModel\n",
+ " _grpo_dir = OUT_DIR / \"grpo_adapter\"\n",
+ " print(f\"final_trainer not in globals; reloading GRPO adapter from {_grpo_dir}...\")\n",
+ " _base, final_tok = FastLanguageModel.from_pretrained(\n",
+ " model_name=MODEL.base_model,\n",
+ " max_seq_length=MODEL.max_seq_length,\n",
+ " load_in_4bit=True,\n",
+ " )\n",
+ " _grpo_model = PeftModel.from_pretrained(_base, str(_grpo_dir), is_trainable=False)\n",
+ " class _TrainerShim:\n",
+ " pass\n",
+ " final_trainer = _TrainerShim()\n",
+ " final_trainer.model = _grpo_model\n",
+ "\n",
+ "qual = await qualitative_rollouts(final_trainer.model, final_tok, TASK_MAP, DRIFT_TASK_IDS)\n",
+ "print(json.dumps(qual, indent=2, default=str))\n",
+ "(OUT_DIR / \"qualitative_rollouts.json\").write_text(json.dumps(qual, indent=2, default=str))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "graphs-md",
+ "metadata": {
+ "id": "graphs-md"
+ },
+ "source": [
+ "## 17.5 · Plot training curves and SFT vs GRPO comparison\n",
+ "\n",
+ "Emits matplotlib PNGs into `OUT_DIR / \"graphs/\"` (also zipped as `graphs.zip` for\n",
+ "easy download):\n",
+ "\n",
+ "1. `01_optuna_history.png` – per-trial objective + best-so-far.\n",
+ "2. `02_optuna_hparams.png` – hparam vs. objective scatter (one subplot per knob).\n",
+ "3. `03_optuna_trial_curves.png` – training loss / reward / reward_std / KL for\n",
+ " every trial, with the winning trial highlighted.\n",
+ "4. `04_final_run_curves.png` – same four metrics for the final GRPO run on the\n",
+ " best Optuna config.\n",
+ "5. `05_sft_vs_grpo_scalar.png` – scalar comparison (success, reward, recovery,\n",
+ " drift repair, hints/solved, steps-to-solve, destructive-fail).\n",
+ "6. `06_success_by_tier.png` / `07_reward_by_tier.png` – per-difficulty-tier\n",
+ " bars, SFT vs SFT+GRPO.\n",
+ "\n",
+ "Each section is guarded so a missing artifact (e.g. Optuna was skipped, or\n",
+ "multi-step eval hasn\\'t run yet) only skips that plot instead of failing.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "graphs-plot",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "graphs-plot",
+ "outputId": "abab8fac-d13a-42e6-edaa-1b8746f8988a"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " saved 01_optuna_history.png\n",
+ " saved 02_optuna_hparams.png\n",
+ " saved 03_optuna_trial_curves.png\n",
+ " saved 04_final_run_curves.png\n",
+ " saved 05_sft_vs_grpo_scalar.png\n",
+ " saved 06_success_by_tier.png\n",
+ " saved 07_reward_by_tier.png\n",
+ "\n",
+ "Graphs: /content/out/graphs (7 PNGs)\n",
+ "Zipped: /content/out/graphs.zip\n"
+ ]
+ }
+ ],
+ "source": [
+ "import json\n",
+ "import math\n",
+ "import shutil\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "\n",
+ "GRAPHS_DIR = OUT_DIR / \"graphs\"\n",
+ "GRAPHS_DIR.mkdir(parents=True, exist_ok=True)\n",
+ "\n",
+ "# Canonical tier ordering so bar plots read hardest-to-easiest consistently.\n",
+ "_TIER_ORDER = [\"warmup\", \"beginner\", \"intermediate\", \"advanced\", \"expert\"]\n",
+ "\n",
+ "\n",
+ "def _tier_key(t: str) -> int:\n",
+ " return _TIER_ORDER.index(t) if t in _TIER_ORDER else 99\n",
+ "\n",
+ "\n",
+ "def _load_log_history(run_dir: Path) -> list[dict]:\n",
+ " \"\"\"Prefer a top-level trainer_state.json (written by trial_objective) and\n",
+ " fall back to the latest checkpoint-N/trainer_state.json (the layout TRL\n",
+ " uses when save_strategy != \"no\", i.e. the final run).\"\"\"\n",
+ " top = run_dir / \"trainer_state.json\"\n",
+ " if top.exists():\n",
+ " return json.loads(top.read_text()).get(\"log_history\", [])\n",
+ " ckpts = sorted(\n",
+ " (d for d in run_dir.glob(\"checkpoint-*\") if d.is_dir()),\n",
+ " key=lambda d: int(d.name.split(\"-\")[-1]),\n",
+ " )\n",
+ " if not ckpts:\n",
+ " return []\n",
+ " state_path = ckpts[-1] / \"trainer_state.json\"\n",
+ " if not state_path.exists():\n",
+ " return []\n",
+ " return json.loads(state_path.read_text()).get(\"log_history\", [])\n",
+ "\n",
+ "\n",
+ "def _series(hist: list[dict], key: str) -> tuple[list[int], list[float]]:\n",
+ " \"\"\"Pull (step, value) pairs for a key from TRL's log_history, skipping\n",
+ " entries that don't carry it (eval rows only have eval_* keys, etc.).\"\"\"\n",
+ " xs, ys = [], []\n",
+ " for row in hist:\n",
+ " if key in row and isinstance(row[key], (int, float)):\n",
+ " xs.append(row.get(\"step\", len(xs)))\n",
+ " ys.append(float(row[key]))\n",
+ " return xs, ys\n",
+ "\n",
+ "\n",
+ "_saved: list[Path] = []\n",
+ "\n",
+ "\n",
+ "def _save(fig, name: str) -> None:\n",
+ " out = GRAPHS_DIR / name\n",
+ " fig.savefig(out, dpi=150, bbox_inches=\"tight\")\n",
+ " plt.close(fig)\n",
+ " _saved.append(out)\n",
+ " print(f\" saved {out.name}\")\n",
+ "\n",
+ "\n",
+ "# ---------- 1) Optuna optimization history ----------\n",
+ "try:\n",
+ " _trials = [t for t in study.trials if t.value is not None]\n",
+ " if not _trials:\n",
+ " raise RuntimeError(\"no completed Optuna trials\")\n",
+ " _nums = [t.number for t in _trials]\n",
+ " _vals = [t.value for t in _trials]\n",
+ " _best = []\n",
+ " _cur = -math.inf\n",
+ " for v in _vals:\n",
+ " _cur = max(_cur, v)\n",
+ " _best.append(_cur)\n",
+ " fig, ax = plt.subplots(figsize=(8, 4))\n",
+ " ax.plot(_nums, _vals, \"o-\", label=\"trial env_reward_mean\")\n",
+ " ax.plot(_nums, _best, \"r--\", label=\"best so far\")\n",
+ " ax.scatter([study.best_trial.number], [study.best_value],\n",
+ " s=120, facecolors=\"none\", edgecolors=\"red\", linewidths=2,\n",
+ " label=f\"best (trial {study.best_trial.number})\")\n",
+ " ax.set_xlabel(\"trial\"); ax.set_ylabel(\"val env_reward_mean\")\n",
+ " ax.set_title(\"Optuna optimization history\")\n",
+ " ax.legend(); ax.grid(alpha=0.3)\n",
+ " _save(fig, \"01_optuna_history.png\")\n",
+ "except Exception as e:\n",
+ " print(f\" skipped 01_optuna_history.png: {e}\")\n",
+ "\n",
+ "\n",
+ "# ---------- 2) Hparam vs objective scatter ----------\n",
+ "try:\n",
+ " _trials = [t for t in study.trials if t.value is not None]\n",
+ " if not _trials:\n",
+ " raise RuntimeError(\"no completed Optuna trials\")\n",
+ " _params = [\"learning_rate\", \"beta\", \"temperature\"]\n",
+ " fig, axes = plt.subplots(1, 3, figsize=(13, 4))\n",
+ " for ax, p in zip(axes.flat, _params):\n",
+ " xs = [t.params[p] for t in _trials if p in t.params]\n",
+ " ys = [t.value for t in _trials if p in t.params]\n",
+ " if not xs:\n",
+ " ax.set_visible(False); continue\n",
+ " ax.scatter(xs, ys, s=35)\n",
+ " # Highlight best trial\n",
+ " if p in study.best_params:\n",
+ " ax.scatter([study.best_params[p]], [study.best_value],\n",
+ " s=120, facecolors=\"none\", edgecolors=\"red\",\n",
+ " linewidths=2, label=\"best\")\n",
+ " ax.legend(fontsize=8)\n",
+ " if p == \"learning_rate\":\n",
+ " ax.set_xscale(\"log\")\n",
+ " ax.set_xlabel(p); ax.set_ylabel(\"env_reward_mean\")\n",
+ " ax.grid(alpha=0.3)\n",
+ " fig.suptitle(\"Hyperparameter vs objective (Optuna)\")\n",
+ " fig.tight_layout()\n",
+ " _save(fig, \"02_optuna_hparams.png\")\n",
+ "except Exception as e:\n",
+ " print(f\" skipped 02_optuna_hparams.png: {e}\")\n",
+ "\n",
+ "\n",
+ "# ---------- 3) Per-trial training curves ----------\n",
+ "try:\n",
+ " _trials = [t for t in study.trials if t.value is not None]\n",
+ " if not _trials:\n",
+ " raise RuntimeError(\"no completed Optuna trials\")\n",
+ " _metrics = [(\"loss\", \"training loss\"),\n",
+ " (\"reward\", \"reward mean\"),\n",
+ " (\"reward_std\", \"reward std (GRPO group)\"),\n",
+ " (\"kl\", \"KL to reference\")]\n",
+ " fig, axes = plt.subplots(2, 2, figsize=(13, 8))\n",
+ " _best_num = study.best_trial.number if study.best_trial else None\n",
+ " _plotted_any = False\n",
+ " for ax, (key, title) in zip(axes.flat, _metrics):\n",
+ " for t in _trials:\n",
+ " trial_dir = OUT_DIR / f\"optuna/trial-{t.number}\"\n",
+ " hist = _load_log_history(trial_dir)\n",
+ " xs, ys = _series(hist, key)\n",
+ " if not xs:\n",
+ " continue\n",
+ " _plotted_any = True\n",
+ " if t.number == _best_num:\n",
+ " ax.plot(xs, ys, color=\"tab:red\", lw=2.2,\n",
+ " label=f\"trial {t.number} (best)\")\n",
+ " else:\n",
+ " ax.plot(xs, ys, alpha=0.45, lw=1.2,\n",
+ " label=f\"trial {t.number}\")\n",
+ " ax.set_title(title); ax.set_xlabel(\"step\")\n",
+ " ax.grid(alpha=0.3)\n",
+ " if not _plotted_any:\n",
+ " raise RuntimeError(\"no per-trial log_history on disk — re-run \"\n",
+ " \"Optuna after the trial_objective patch\")\n",
+ " # One legend for the whole figure\n",
+ " handles, labels = axes.flat[0].get_legend_handles_labels()\n",
+ " if handles:\n",
+ " fig.legend(handles, labels, loc=\"lower center\",\n",
+ " ncol=min(6, len(handles)), fontsize=8,\n",
+ " bbox_to_anchor=(0.5, -0.02))\n",
+ " fig.suptitle(\"Optuna trial training curves\")\n",
+ " fig.tight_layout(rect=(0, 0.04, 1, 1))\n",
+ " _save(fig, \"03_optuna_trial_curves.png\")\n",
+ "except Exception as e:\n",
+ " print(f\" skipped 03_optuna_trial_curves.png: {e}\")\n",
+ "\n",
+ "\n",
+ "# ---------- 4) Final GRPO run curves ----------\n",
+ "try:\n",
+ " hist = _load_log_history(FINAL_RUN_DIR)\n",
+ " if not hist:\n",
+ " raise RuntimeError(f\"no log_history under {FINAL_RUN_DIR}\")\n",
+ " _metrics = [(\"loss\", \"training loss\"),\n",
+ " (\"reward\", \"reward mean\"),\n",
+ " (\"reward_std\", \"reward std (GRPO group)\"),\n",
+ " (\"kl\", \"KL to reference\"),\n",
+ " (\"learning_rate\", \"learning rate\"),\n",
+ " (\"completion_length\", \"completion length (tokens)\")]\n",
+ " fig, axes = plt.subplots(3, 2, figsize=(13, 10))\n",
+ " for ax, (key, title) in zip(axes.flat, _metrics):\n",
+ " xs, ys = _series(hist, key)\n",
+ " if not xs:\n",
+ " ax.set_visible(False); continue\n",
+ " ax.plot(xs, ys, color=\"tab:blue\")\n",
+ " ax.set_title(title); ax.set_xlabel(\"step\")\n",
+ " ax.grid(alpha=0.3)\n",
+ " if key == \"learning_rate\":\n",
+ " ax.set_yscale(\"log\")\n",
+ " fig.suptitle(\"Final GRPO run — best Optuna config\")\n",
+ " fig.tight_layout()\n",
+ " _save(fig, \"04_final_run_curves.png\")\n",
+ "except Exception as e:\n",
+ " print(f\" skipped 04_final_run_curves.png: {e}\")\n",
+ "\n",
+ "\n",
+ "# ---------- 5) SFT vs GRPO scalar comparison (multi-step eval) ----------\n",
+ "try:\n",
+ " sft = json.loads((OUT_DIR / \"baseline_multi_step.json\").read_text())\n",
+ " grpo = json.loads((OUT_DIR / \"grpo_multi_step.json\").read_text())\n",
+ " _keys = [\"overall_success_rate\", \"overall_reward_mean\",\n",
+ " \"recovery_rate\", \"drift_repair_rate\",\n",
+ " \"hints_per_solved\", \"steps_to_solve\",\n",
+ " \"destructive_fail_rate\"]\n",
+ " x = np.arange(len(_keys))\n",
+ " fig, ax = plt.subplots(figsize=(12, 5))\n",
+ " w = 0.38\n",
+ " sft_vals = [float(sft.get(k, 0.0)) for k in _keys]\n",
+ " grpo_vals = [float(grpo.get(k, 0.0)) for k in _keys]\n",
+ " b1 = ax.bar(x - w/2, sft_vals, width=w, label=\"SFT only\", color=\"tab:gray\")\n",
+ " b2 = ax.bar(x + w/2, grpo_vals, width=w, label=\"SFT + GRPO\", color=\"tab:blue\")\n",
+ " for bars, vals in ((b1, sft_vals), (b2, grpo_vals)):\n",
+ " for bar, v in zip(bars, vals):\n",
+ " ax.text(bar.get_x() + bar.get_width() / 2, v,\n",
+ " f\"{v:.2f}\", ha=\"center\", va=\"bottom\", fontsize=8)\n",
+ " ax.set_xticks(x); ax.set_xticklabels(_keys, rotation=25, ha=\"right\")\n",
+ " ax.set_title(\"Multi-step eval: SFT vs SFT+GRPO\")\n",
+ " ax.legend(); ax.grid(axis=\"y\", alpha=0.3)\n",
+ " fig.tight_layout()\n",
+ " _save(fig, \"05_sft_vs_grpo_scalar.png\")\n",
+ "except Exception as e:\n",
+ " print(f\" skipped 05_sft_vs_grpo_scalar.png: {e}\")\n",
+ "\n",
+ "\n",
+ "# ---------- 6/7) Per-tier SFT vs GRPO ----------\n",
+ "def _per_tier_plot(metric_key: str, ylabel: str, fname: str) -> None:\n",
+ " try:\n",
+ " sft = json.loads((OUT_DIR / \"baseline_multi_step.json\").read_text())\n",
+ " grpo = json.loads((OUT_DIR / \"grpo_multi_step.json\").read_text())\n",
+ " sft_t = sft.get(metric_key, {}) or {}\n",
+ " grpo_t = grpo.get(metric_key, {}) or {}\n",
+ " tiers = sorted(set(sft_t) | set(grpo_t), key=_tier_key)\n",
+ " if not tiers:\n",
+ " raise RuntimeError(f\"no tiers in {metric_key}\")\n",
+ " x = np.arange(len(tiers))\n",
+ " fig, ax = plt.subplots(figsize=(9, 4.5))\n",
+ " w = 0.38\n",
+ " ax.bar(x - w/2, [float(sft_t.get(t, 0.0)) for t in tiers],\n",
+ " width=w, label=\"SFT only\", color=\"tab:gray\")\n",
+ " ax.bar(x + w/2, [float(grpo_t.get(t, 0.0)) for t in tiers],\n",
+ " width=w, label=\"SFT + GRPO\", color=\"tab:blue\")\n",
+ " ax.set_xticks(x); ax.set_xticklabels(tiers)\n",
+ " ax.set_title(f\"{metric_key} by tier\")\n",
+ " ax.set_ylabel(ylabel)\n",
+ " ax.legend(); ax.grid(axis=\"y\", alpha=0.3)\n",
+ " fig.tight_layout()\n",
+ " _save(fig, fname)\n",
+ " except Exception as e:\n",
+ " print(f\" skipped {fname}: {e}\")\n",
+ "\n",
+ "\n",
+ "_per_tier_plot(\"success_by_tier\", \"success rate\", \"06_success_by_tier.png\")\n",
+ "_per_tier_plot(\"reward_by_tier\", \"mean terminal reward\", \"07_reward_by_tier.png\")\n",
+ "\n",
+ "\n",
+ "# ---------- Zip for easy download ----------\n",
+ "if _saved:\n",
+ " _zip_base = str(OUT_DIR / \"graphs\")\n",
+ " _zip_path = shutil.make_archive(_zip_base, \"zip\", root_dir=GRAPHS_DIR)\n",
+ " print(f\"\\nGraphs: {GRAPHS_DIR} ({len(_saved)} PNGs)\")\n",
+ " print(f\"Zipped: {_zip_path}\")\n",
+ "else:\n",
+ " print(\"\\nNo graphs were produced — every section was skipped.\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3751583b",
+ "metadata": {
+ "id": "3751583b"
+ },
+ "source": [
+ "## 18 · Publish the GRPO adapter to a new HF Hub repo\n",
+ "\n",
+ "Pushes **only** to `Sizzing/aws-rl-grpo-qwen25coder3b-adapter`. The existing SFT adapter repo `Sizzing/aws-rl-sft-qwen25coder3b-adapter` is never touched — both coexist on Hub so reviewers can load either and compare side by side.\n",
+ "\n",
+ "The model card notes the lineage (`base_model = SFT adapter`) so anyone opening the repo on Hub sees immediately that this is a second-stage RL fine-tune."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "7fbbbdd6",
+ "metadata": {
+ "id": "7fbbbdd6",
+ "outputId": "a62895fb-a8f0-4c62-c77a-0ee2a5791e63",
+ "colab": {
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+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "README.md: 0.00B [00:00, ?B/s]"
+ ],
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+ " ...adapter_model.safetensors: 2%|1 | 563kB / 29.5MB "
+ ],
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ " ...mp7c5v_5a_/tokenizer.json: 100%|##########| 11.4MB / 11.4MB "
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "cc03a92eb183446996cc113b8403567c"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Pushed to https://huggingface.co/Sizzing/aws-rl-grpo-qwen25coder3b-adapter\n",
+ "SFT adapter at Sizzing/aws-rl-sft-qwen25coder3b-adapter untouched — both models available on Hub.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from datetime import datetime, timezone\n",
+ "\n",
+ "\n",
+ "def write_model_card(adapter_dir: Path, model_spec: ModelSpec,\n",
+ " cfg: TrainingConfig, grpo: RichMetrics, sft: RichMetrics) -> Path:\n",
+ " \"\"\"Emit a README.md for the pushed repo documenting training recipe + metrics.\"\"\"\n",
+ " card = f\"\"\"---\n",
+ "library_name: peft\n",
+ "base_model: {model_spec.sft_adapter}\n",
+ "pipeline_tag: text-generation\n",
+ "tags: [grpo, lora, aws, reinforcement-learning]\n",
+ "---\n",
+ "\n",
+ "# aws-rl-grpo-qwen25coder3b-adapter\n",
+ "\n",
+ "GRPO (Group Relative Policy Optimization) LoRA adapter continuing from\n",
+ "[`{model_spec.sft_adapter}`](https://huggingface.co/{model_spec.sft_adapter}).\n",
+ "Trained single-step with live AWS RL env rewards; evaluated multi-step.\n",
+ "\n",
+ "## How to load\n",
+ "\n",
+ "```python\n",
+ "from unsloth import FastLanguageModel\n",
+ "from peft import PeftModel\n",
+ "\n",
+ "base, tok = FastLanguageModel.from_pretrained(\n",
+ " \"{model_spec.base_model}\", max_seq_length={model_spec.max_seq_length}, load_in_4bit=True,\n",
+ ")\n",
+ "model = PeftModel.from_pretrained(base, \"{model_spec.grpo_adapter}\")\n",
+ "FastLanguageModel.for_inference(model)\n",
+ "```\n",
+ "\n",
+ "## Training recipe\n",
+ "\n",
+ "| Knob | Value |\n",
+ "|-----------------------|--------------------|\n",
+ "| learning_rate | {cfg.learning_rate:.2e} |\n",
+ "| beta (KL coef) | {cfg.beta:.3f} |\n",
+ "| num_generations (G) | {cfg.num_generations} |\n",
+ "| temperature | {cfg.temperature:.2f} |\n",
+ "| max_completion_length | {cfg.max_completion_length} |\n",
+ "| per-device batch | {cfg.per_device_train_batch_size} x {cfg.gradient_accumulation_steps} accum |\n",
+ "\n",
+ "## Multi-step eval (reserve + drift)\n",
+ "\n",
+ "| Metric | SFT baseline | GRPO | Delta |\n",
+ "|-----------------------|-------------:|-------:|--------:|\n",
+ "| overall_success_rate | {sft.overall_success_rate:.3f} | {grpo.overall_success_rate:.3f} | {grpo.overall_success_rate - sft.overall_success_rate:+.3f} |\n",
+ "| overall_reward_mean | {sft.overall_reward_mean:.3f} | {grpo.overall_reward_mean:.3f} | {grpo.overall_reward_mean - sft.overall_reward_mean:+.3f} |\n",
+ "| hints_per_solved | {sft.hints_per_solved:.3f} | {grpo.hints_per_solved:.3f} | {grpo.hints_per_solved - sft.hints_per_solved:+.3f} |\n",
+ "| recovery_rate | {sft.recovery_rate:.3f} | {grpo.recovery_rate:.3f} | {grpo.recovery_rate - sft.recovery_rate:+.3f} |\n",
+ "| drift_repair_rate | {sft.drift_repair_rate:.3f} | {grpo.drift_repair_rate:.3f} | {grpo.drift_repair_rate - sft.drift_repair_rate:+.3f} |\n",
+ "| steps_to_solve | {sft.steps_to_solve:.3f} | {grpo.steps_to_solve:.3f} | {grpo.steps_to_solve - sft.steps_to_solve:+.3f} |\n",
+ "\n",
+ "Trained {datetime.now(timezone.utc).isoformat(timespec='minutes')} on {RT.gpu_name}.\n",
+ "\"\"\"\n",
+ " card_path = adapter_dir / \"README.md\"\n",
+ " card_path.write_text(card)\n",
+ " return card_path\n",
+ "\n",
+ "\n",
+ "# Write card locally, then push both adapter files + card\n",
+ "adapter_local = OUT_DIR / \"grpo_adapter\"\n",
+ "write_model_card(adapter_local, MODEL, best_cfg, grpo_metrics, sft_metrics)\n",
+ "\n",
+ "commit_msg = f\"GRPO run {datetime.now(timezone.utc).isoformat(timespec='minutes')}\"\n",
+ "final_trainer.model.push_to_hub(\n",
+ " MODEL.grpo_adapter, private=True, token=HF_TOKEN, commit_message=commit_msg,\n",
+ ")\n",
+ "final_tok.push_to_hub(\n",
+ " MODEL.grpo_adapter, private=True, token=HF_TOKEN, commit_message=commit_msg,\n",
+ ")\n",
+ "HfApi(token=HF_TOKEN).upload_file(\n",
+ " path_or_fileobj=str(adapter_local / \"README.md\"),\n",
+ " path_in_repo=\"README.md\",\n",
+ " repo_id=MODEL.grpo_adapter,\n",
+ " commit_message=commit_msg,\n",
+ ")\n",
+ "print(f\"Pushed to https://huggingface.co/{MODEL.grpo_adapter}\")\n",
+ "print(f\"SFT adapter at {MODEL.sft_adapter} untouched — both models available on Hub.\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "62c7435b",
+ "metadata": {
+ "id": "62c7435b"
+ },
+ "source": [
+ "## 19 · Clean shutdown + artifact bundle\n",
+ "\n",
+ "Releases the GPU, tars `OUT_DIR` into a single downloadable archive (Colab `files.download`). Nothing else needs to be killed — the env server is hosted externally.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "ae82230e",
+ "metadata": {
+ "id": "ae82230e",
+ "outputId": "ba084f01-0c7b-444a-8984-a7a0369b0ab8",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 263
+ }
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "19:05:30 | INFO | grpo | Found 10 PNGs in /content/out/graphs:\n",
+ "19:05:30 | INFO | grpo | 00_optuna_history.png (38 KB)\n",
+ "19:05:30 | INFO | grpo | 00_optuna_importances.png (39 KB)\n",
+ "19:05:30 | INFO | grpo | 00_optuna_parallel.png (97 KB)\n",
+ "19:05:30 | INFO | grpo | 01_optuna_history.png (53 KB)\n",
+ "19:05:30 | INFO | grpo | 02_optuna_hparams.png (52 KB)\n",
+ "19:05:30 | INFO | grpo | 03_optuna_trial_curves.png (270 KB)\n",
+ "19:05:30 | INFO | grpo | 04_final_run_curves.png (254 KB)\n",
+ "19:05:30 | INFO | grpo | 05_sft_vs_grpo_scalar.png (84 KB)\n",
+ "19:05:30 | INFO | grpo | 06_success_by_tier.png (33 KB)\n",
+ "19:05:30 | INFO | grpo | 07_reward_by_tier.png (34 KB)\n",
+ "19:05:41 | INFO | grpo | Zip bundle: /content/out.zip (122.2 MB)\n",
+ "19:05:41 | INFO | grpo | HF Hub: SFT=https://huggingface.co/Sizzing/aws-rl-sft-qwen25coder3b-adapter GRPO=https://huggingface.co/Sizzing/aws-rl-grpo-qwen25coder3b-adapter\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "application/javascript": [
+ "\n",
+ " async function download(id, filename, size) {\n",
+ " if (!google.colab.kernel.accessAllowed) {\n",
+ " return;\n",
+ " }\n",
+ " const div = document.createElement('div');\n",
+ " const label = document.createElement('label');\n",
+ " label.textContent = `Downloading \"${filename}\": `;\n",
+ " div.appendChild(label);\n",
+ " const progress = document.createElement('progress');\n",
+ " progress.max = size;\n",
+ " div.appendChild(progress);\n",
+ " document.body.appendChild(div);\n",
+ "\n",
+ " const buffers = [];\n",
+ " let downloaded = 0;\n",
+ "\n",
+ " const channel = await google.colab.kernel.comms.open(id);\n",
+ " // Send a message to notify the kernel that we're ready.\n",
+ " channel.send({})\n",
+ "\n",
+ " for await (const message of channel.messages) {\n",
+ " // Send a message to notify the kernel that we're ready.\n",
+ " channel.send({})\n",
+ " if (message.buffers) {\n",
+ " for (const buffer of message.buffers) {\n",
+ " buffers.push(buffer);\n",
+ " downloaded += buffer.byteLength;\n",
+ " progress.value = downloaded;\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " const blob = new Blob(buffers, {type: 'application/binary'});\n",
+ " const a = document.createElement('a');\n",
+ " a.href = window.URL.createObjectURL(blob);\n",
+ " a.download = filename;\n",
+ " div.appendChild(a);\n",
+ " a.click();\n",
+ " div.remove();\n",
+ " }\n",
+ " "
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ ""
+ ],
+ "application/javascript": [
+ "download(\"download_464556f1-c59e-4dd9-8519-ffe991d7155a\", \"out.zip\", 122164368)"
+ ]
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "import shutil\n",
+ "from pathlib import Path\n",
+ "\n",
+ "# Verify graphs were exported\n",
+ "graphs_dir = OUT_DIR / \"graphs\"\n",
+ "if graphs_dir.exists():\n",
+ " pngs = sorted(graphs_dir.glob(\"*.png\"))\n",
+ " log.info(\"Found %d PNGs in %s:\", len(pngs), graphs_dir)\n",
+ " for p in pngs:\n",
+ " log.info(\" %s (%d KB)\", p.name, p.stat().st_size // 1024)\n",
+ "else:\n",
+ " log.warning(\"No graphs dir at %s — run the matplotlib plot cell first.\", graphs_dir)\n",
+ "\n",
+ "# Release GPU\n",
+ "try:\n",
+ " free_model(final_trainer)\n",
+ " del final_trainer, final_model, final_tok\n",
+ "except Exception:\n",
+ " pass\n",
+ "gc.collect(); torch.cuda.empty_cache()\n",
+ "\n",
+ "# Build a single zip of OUT_DIR (grpo_adapter, JSON metrics, graphs/, etc.)\n",
+ "zip_path = shutil.make_archive(\n",
+ " base_name=str(OUT_DIR.parent / OUT_DIR.name),\n",
+ " format=\"zip\",\n",
+ " root_dir=OUT_DIR,\n",
+ ")\n",
+ "log.info(\"Zip bundle: %s (%.1f MB)\",\n",
+ " zip_path, Path(zip_path).stat().st_size / 1e6)\n",
+ "log.info(\"HF Hub: SFT=https://huggingface.co/%s GRPO=https://huggingface.co/%s\",\n",
+ " MODEL.sft_adapter, MODEL.grpo_adapter)\n",
+ "\n",
+ "if IS_COLAB:\n",
+ " try:\n",
+ " from google.colab import files\n",
+ " files.download(zip_path)\n",
+ " except Exception as e:\n",
+ " log.warning(\"Colab auto-download skipped: %s. Download manually from %s\",\n",
+ " e, zip_path)"
+ ]
+ },
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+ "cell_type": "code",
+ "execution_count": null,
+ "id": "15b65aff",
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index 5ebfa927deb5bcb986105c5e152c9ce485d43b52..4fb1f9800442ca2a1f3549ffe0a9ecdff30499d7 100644
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