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sandeep123/mathbf-grpo-temp12-step900
mathbf-grpo-temp12-step900 is a machine learning model from sandeep123. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
GRPO + temperature 1.2 baseline on ScienceQA, trained with GRPO on Qwen/Qwen2.5-Math-1.5B.
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From the Hugging Face model README
GRPO + temperature 1.2 baseline on ScienceQA, trained with GRPO on Qwen/Qwen2.5-Math-1.5B.
Selected as best validation pass@1 for this arm (rank 1).
Exploration via the sampling distribution on MATH (math_boxfix). Trained at rollout temperature 1.2 but VALIDATED at 1.0 like every other arm. Trained under the FULL boxfix protocol: the prompt demands numbered Step 1..n reasoning followed by \boxed{X} on its own line with nothing after it, and the 0.03 format reward pays for exactly that structure. Supersedes the earlier suite whose prompt and reward disagreed.
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 on a sibling task.
from vllm import LLM, SamplingParams
llm = LLM(model="sandeep123/mathbf-grpo-temp12-step900", dtype="bfloat16", max_model_len=1536)
params = SamplingParams(n=6, temperature=1.0, top_p=1.0, top_k=-1, max_tokens=1024)
out = llm.generate([prompt_text], sampling_params=params) # raw string, not llm.chat()
| metric | value |
|---|---|
| pass@1 | 0.7904 |
| pass@6 | 0.9492 |
| step | 900 |
Answer extraction (pre-registered). An answer is the content of the final
\boxed{}; if absent, the last standalone A-E token. Responses with no
extractable answer are scored incorrect, and all K rollouts stay in the
denominator. This is ScienceQA's answer-choice accuracy, reported as
"sampled answer accuracy (pass@1)".
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.
| dataset | ScienceQA (scienceqa_boxfix) |
| epochs / steps | 25 / 1250 |
| batch / rollouts | 128 prompts, K=6 |
| learning rate | 1e-6 constant |
| KL (in-reward) | 0.01 |
| max prompt / response | 512 / 1024 tokens |
| format reward | 0.03, constant, no decay |
| seed | 42 |
Arm-specific: entropy_coeff=0.0, clip=0.2/0.2, rollout temperature=1.2.
Quality-optimal and diversity-optimal checkpoints differ substantially: for these runs the best-pass@1 checkpoint lands near step 1000-1200 while the best-pass@6 checkpoint is near step 200-500. Both are published so that diversity results are not reported from a checkpoint chosen purely for accuracy.