{"slug": "fixing-grpo-s-credit-assignment-problem-without-evaluating-every-step", "title": "Fixing GRPO's credit assignment problem without evaluating every step", "summary": "A 28 Sep 2026 arXiv paper introduces ProVer, a framework that targets potentially pivotal decisions for fine-grained credit assignment in agentic reinforcement learning, addressing what the authors call GRPO's uniform assignment of trajectory-level advantages to all policy tokens. ProVer uses an agentic judge to contrast successful and failed trajectories and propose a segment, then verifies that segment by estimating its advantage from the difference in terminal success rates between current-policy continuations sampled before and after it, incorporating positive estimates into the GRPO advantages of policy tokens within the segment. Across ALFWorld, WebShop, and SearchQA, ProVer achieved the strongest average performance at both model scales, with relative improvements over GRPO of 9.91% for Qwen3.5-2B and 7.12% for Qwen3.5-4B.", "body_md": "# Computer Science > Computation and Language\n\n  [Submitted on 28 Sep 2026]\n\n# Title:Targeting Pivotal Decisions for Credit Assignment in Agentic Reinforcement Learning\n\n[View PDF](https://arxiv.org/pdf/2609.36178)\n\n[HTML (experimental)](https://arxiv.org/html/2609.36178v1)\n\nAbstract:Group Relative Policy Optimization (GRPO) has become a promising approach for training large language model agents. However, its uniform assignment of trajectory-level advantages to all policy tokens fails to distinguish consequential decisions from less relevant ones, obscuring which intermediate decisions contributed to success. We introduce ProVer, a framework that targets potentially pivotal decisions for fine-grained credit assignment in agentic reinforcement learning. Given a rollout group, an agentic judge contrasts successful and failed trajectories to propose a segment potentially responsible for their divergent outcomes. Rather than directly trusting the judge's assessment, ProVer verifies the proposed segment by estimating its advantage from the difference in terminal success rates between current-policy continuations sampled before and after the segment. Positive estimates are then incorporated into the GRPO advantages of policy tokens within the proposed segment. By using model judgment only to select where to verify, ProVer grounds local credit in observed outcomes without exhaustively evaluating every intermediate state. Across ALFWorld, WebShop, and SearchQA, ProVer achieves the strongest average performance at both model scales, with relative improvements over GRPO of 9.91% and 7.12% for Qwen3.5-2B and Qwen3.5-4B, respectively. Further analyses demonstrate that informed segment selection improves policy training with modest additional generation overhead, even without a frontier-scale judge model, highlighting the effectiveness and efficiency of selectively targeting pivotal decisions for fine-grained credit assignment in agentic reinforcement learning.\n    \n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/fixing-grpo-s-credit-assignment-problem-without-evaluating-every-step", "canonical_source": "https://arxiv.org/abs/2609.36178", "published_at": "2026-10-02 14:36:04+00:00", "updated_at": "2026-10-02 16:36:58.263788+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-agents", "ai-research"], "entities": ["ProVer", "GRPO", "Qwen3.5-2B", "Qwen3.5-4B", "ALFWorld", "WebShop", "SearchQA", "arXiv"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/fixing-grpo-s-credit-assignment-problem-without-evaluating-every-step", "markdown": "https://wpnews.pro/news/fixing-grpo-s-credit-assignment-problem-without-evaluating-every-step.md", "text": "https://wpnews.pro/news/fixing-grpo-s-credit-assignment-problem-without-evaluating-every-step.txt", "jsonld": "https://wpnews.pro/news/fixing-grpo-s-credit-assignment-problem-without-evaluating-every-step.jsonld"}}