aops-grpo-cliphigh-step300
GRPO + Clip-Higher baseline on AoPS, trained with GRPO on Qwen/Qwen2.5-Math-1.5B.
Selected as best validation pass@6 for this arm (rank 1).
Decoupled PPO clip bounds on AoPS: lower 1-0.2, upper 1+0.28 (DAPO). One of DAPO's four components; not full DAPO.
Training data and format reward
| dataset | AoPS (aops_boxfix) |
| epochs / steps | 10 / 1450 |
| batch / rollouts | 128 prompts, K=6 |
| learning rate | 1e-6 constant |
| KL (in-reward) | 0.01 |
| max prompt / response | 1024 / 2048 tokens |
| format reward | 0.03, constant, no decay |
| seed | 42 |
Full boxfix protocol: the prompt demands Step 1: ... Step n: followed by \boxed{X} on its own line with nothing after it, and the 0.03 format reward pays for exactly that structure.
Arm-specific: entropy_coeff=0.0, clip=0.2/0.28, rollout temperature=1.0.
Do not apply a chat template
Trained on raw prompt text. verl's RLHFDataset has apply_chat_template=False
and it was never enabled. Applying Qwen2.5-Math's chat template at inference creates
a train/eval mismatch measured at roughly 19 points of pass@1.
from vllm import LLM, SamplingParams
llm = LLM(model="sandeep123/aops-grpo-cliphigh-step300", dtype="bfloat16", max_model_len=3072)
params = SamplingParams(n=6, temperature=1.0, top_p=1.0, top_k=-1, max_tokens=2048)
out = llm.generate([prompt_text], sampling_params=params) # raw string, not llm.chat()
Validation metrics at this checkpoint
| metric | value |
|---|---|
| pass@1 | 0.2292 |
| pass@6 | 0.4258 |
| step | 300 |
Answer extraction (pre-registered). An answer is the content of the final \boxed{}, compared by mathematical equivalence. Responses with no extractable answer are scored incorrect, and all K rollouts
stay in the denominator.
Validation uses 256 held-out prompts, K=6, temperature 1.0, seed 42 -- pinned in code so every arm, including the temperature-1.2 arm, is scored under identical decoding.
Checkpoint selection
Quality-optimal and diversity-optimal checkpoints differ substantially, so both the best-pass@1 and best-pass@6 checkpoints are published for every arm. Diversity results are therefore never reported from a checkpoint chosen purely for accuracy.
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