GRPO-style RL gains denser feedback from step-level credit assignment, with 54.7→60.7 pass@1 on Qwen3-0.6B
dejavucoder · x · 2026-07-26
What the post argues
The article explores adding paragraph-level credit assignment to GRPO-style RL by tracking how each reasoning prefix changes a scorer’s relative likelihood of correct vs. incorrect answers from the rollout group.
Reported results
- On Countdown with Qwen3-0.6B, a frozen scorer improves pass@1 after 30 steps from 54.7 to 60.7 across three runs versus the GRPO baseline.
- For longer training runs, the advantage fades: 60.4 baseline vs. 59.4 shaped at step 240.
- A moving scorer reaches a higher step-240 performance of 63.9.
Efficiency note
After a target-span KV-cache optimization in vLLM, the frozen-scorer runs drop from 216 minutes to 97 minutes, while the baseline takes 93 minutes. The author’s point is that denser learning signals can be added with minimal overhead.
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