Credit Assignment with Resets in Language Model Reasoning
Contemporary reinforcement learning with verifiable reward methods post-train language models on multi-step reasoning by assigning a single outcome reward uniformly across all tokens in a trajectory. Such uniform assignment ignores which steps contributed to success or failure. Improving credit assignment can address this limitation by enabling targeted refinement of faulty reasoning steps, rather than updating entire trajectories uniformly. Resets are one such simple mechanism, enabling more precise credit assignment by returning to an intermediate state and resampling counterfactual continuations, so that outcome differences can be attributed to decisions made at that point. We propose ...
| Authors | Ankur Samanta, Akshayaa Magesh, Ayush Jain et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 May 2026 |
| Last updated at source | 26 May 2026 |
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