You need to agree to share your contact information to access this dataset

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this dataset content.

Generalized C++ GRPO20 Sept 1 — iter 19

Portable PEFT LoRA checkpoints from generalized-cpp-kernel-grpo20-spot-20260901-065545.

Selected checkpoint

The selected release checkpoint is iter_0000019 (latest preserved checkpoint of the run; 20/20 rollouts completed, training gate passed).

Field Value
Base model zai-org/GLM-4.7-Flash@7dd20894a642a0aa287e9827cb1a1f7f91386b67
Adapter SHA-256 0c0da7a1c3553dfda66af622c8eb5002f3d7418321cfda261414ef020353240c
Adapter config SHA-256 0bd6d85f88fc42fefa52627b3c261f1ad58bb2c9519332ae8034dd5dffe2498e
LoRA rank / alpha 16 / 32
Warm start phone-number-grpo20-iter14 hybrid adapter (source SHA-256 62fa190ad26e30fc1b5dd9543936ef549a49dd8cfa8220e4af726a1d499e575a)
Source commit 08be9518d7b3609312c51c686b139d011d802d21
Planned updates 20 (completed 20/20 rollouts; wall 10,771 s)
Training data Generalized_Cpp_GRPO20_Sept1_train.jsonl, 6 rows
Training-data SHA-256 566ea43aad4f6419b345e0295288c27b374575ce4db30cbd2d6b7510bf86ffbe
Training-manifest SHA-256 94803063131c19cbf6f89698e350bab5e0f4a7d2ffa87ba0a06077e186b668bf
Runtime image us-central1-docker.pkg.dev/lifeandhalf-24122025/w8-biayn/glm47-full-v5-unadmitted-grpo@sha256:5df1c41efb6062a292ac68a49d68ad955d9f8209c182d3fc2f7e8c634ca076a2
W&B glm47-generalized-cpp-grpo/generalized-cpp-kernel-grpo20-spot-20260901-065545

Post-training evaluations

Fixed26 (fixed26-contract-v2), thinking enabled, temperature 0.7, top-p 1.0, 32,768-token response limit, tries=2. All four planned trials completed (t01-t04) and every receipt passes the integrity gate (status=complete, 26 terminal tasks, 26 unique testcases, tries=2). t04 required one shard resume; per-task outcomes reproduce its aggregate receipt exactly.

Checkpoint Pass@1 trial scores Pass@1 mean (4 trials) Turn-2 trial scores Turn-2 mean (4 trials)
iter_0000019 12, 8, 9, 11 10/26 13, 15, 13, 15 14/26
Checkpoint Pass@1 SD; range; task-bootstrap 95% CI (out of 26) Turn-2 SD; range; task-bootstrap 95% CI (out of 26) Conditional turn-2 recovery
iter_0000019 1.83; 8-12; 6.25-13.75 1.15; 13-15; 9.75-18.25 16/64 (25%; CI 13.16-40.38%)

The matched Phone Number kernel12 GRPO20 iter_0000019 comparison cohort (required by RELEASE_PLAN.md) has not been evaluated yet and is pending.

Receipts, shard receipts, per-task results, transcripts, and logs are under evaluations/.

Training data

6 generalized-cpp-v1 rows: clock, complex-numbers, crypto-square, grade-school, kindergarten-garden, perfect-numbers. All six task IDs explicitly overlap the Fixed26 evaluation tasks (authorized training target per the data manifest split contract); consult training_data_manifest.json before comparing results.

Checkpoints

Every checkpoint directory contains only the two portable inference artifacts: adapter_config.json and adapter_model.bin (9,741 tensors). Megatron tensor-parallel shards, optimizer state, and other training-only files are intentionally omitted. Note the portable adapter includes 207 layer-47 tensors; the serving adapter used in evaluation strips them (serving SHA-256 6a7b3247245ab652fca0dbf4e628de05e5e636c17770fcb34f5f3ffb9961d4d5).

Checkpoint Adapter SHA-256
iter_0000004 3f041b0e5d7d153cc3e14ee646dd2c476662baec4e34bde30eeeda597aa23808
iter_0000009 a1267c25b00b7599ae892abdbc6c89ef872285dd10d44e7c6ff27c8b0f47ffa8
iter_0000014 ada21f6499d117ae9a1ab7d18eb2089ac752976026bc95c4f6aee057ace8216e
iter_0000019 0c0da7a1c3553dfda66af622c8eb5002f3d7418321cfda261414ef020353240c

Loading

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "zai-org/GLM-4.7-Flash"
checkpoint = "HimanshuPathak/grpo_iter19_sept_1"
subfolder = "checkpoints/iter_0000019/adapter"

tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(base, trust_remote_code=True)
model = PeftModel.from_pretrained(model, checkpoint, subfolder=subfolder)

Reproduction and evidence

The release includes the exact training JSONL and manifest, the training gate, checkpoint/artifact manifests, launch configs, and per-trial aggregate + shard receipts with per-task results and transcripts. Checksum files bind each evidence bundle (evaluations/.../checksums.sha256).

These are assisted Fixed26 regression results on a complete four-trial cohort, not pristine held-out benchmark claims. The training data explicitly overlaps six Fixed26 task IDs; see above.

Downloads last month
30