Credit Assignment with Resets in Language Model Reasoning
The article discusses advancements in credit assignment methods for language model reasoning in reinforcement learning. It introduces two new techniques, Random-Reset Policy Optimization (RRPO) and Self-Reset Policy Optimization (SRPO), which aim to improve the precision of credit assignment. The authors demonstrate that SRPO consistently outperforms traditional methods by allowing the model to self-localize errors and learn from them effectively.
- ▪Contemporary reinforcement learning assigns a single outcome reward uniformly across all tokens, which can overlook specific contributions to success or failure.
- ▪The proposed methods, RRPO and SRPO, enable targeted refinement of faulty reasoning steps by utilizing resets to attribute outcome differences to specific decisions.
- ▪SRPO has shown consistent improvements over standard GRPO and RRPO by sampling multiple suffix continuations at a self-localized reset.
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Computer Science > Artificial Intelligence arXiv:2605.25507 (cs) [Submitted on 25 May 2026] Title:Credit Assignment with Resets in Language Model Reasoning Authors:Ankur Samanta, Akshayaa Magesh, Ayush Jain, Youliang Yu, Daniel Jiang, Kavosh Asadi, Daniel Jiang, Kaveh Hassani, Paul Sajda, Jalaj Bhandari, Yonathan Efroni View a PDF of the paper titled Credit Assignment with Resets in Language Model Reasoning, by Ankur Samanta and 10 other authors View PDF HTML (experimental) Abstract: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.
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