Targeting Pivotal Decisions for Credit Assignment in Agentic Reinforcement Learning
On the map: LLM reasoning → Reinforcement learning for reasoning
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 33 of its 38 references that are in the map. First posted on arXiv 28 Sep 2026.
Builds on
33 of 38 references.
Closest ancestors · 9
Same direction, newest first
- 2026Enhancing LLM-based Search Agents via Contribution Weighted Group Relative Policy Optimization15 Apr
- 2026Save the Good Prefix: Precise Error Penalization via Process-Supervised RL to Enhance LLM Reasoning26 Jan
- 2026Reinforced Efficient Reasoning via Semantically Diverse Exploration8 Jan
- 2025Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback3 Jun
- 2025Segment Policy Optimization: Effective Segment-Level Credit Assignment in RL for Large Language Models29 May
- 2025Group-in-Group Policy Optimization for LLM Agent Training16 May
- 2025Process Reinforcement through Implicit Rewards3 Feb
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning22 Jan
- 2023Let's Verify Step by Step31 May
Other references · 24
- 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
- 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,686
- 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,895
- 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 1,908
- 2025Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learningcited by 1,371
- 2020Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Stepscited by 1,259
- 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,197
- 2020ALFWorld: Aligning Text and Embodied Environments for Interactive Learningcited by 1,143
- 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,103
- 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
- 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 893
- 2025ReTool: Reinforcement Learning for Strategic Tool Use in LLMscited by 377
12 more
- 2025RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learningcited by 357
- 2025ToolRL: Reward is All Tool Learning Needscited by 342
- 2024VinePPO: Refining Credit Assignment in RL Training of LLMscited by 168
- 2025WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learningcited by 162
- 2016An Actor-Critic Algorithm for Sequence Predictioncited by 158
- 2025TreeRL: LLM Reinforcement Learning with On-Policy Tree Searchcited by 75
- 2025Tree Search for LLM Agent Reinforcement Learningcited by 73
- 2025CriticSearch: Fine-Grained Credit Assignment for Search Agents via a Retrospective Criticcited by 29
- 2024Planning and Editing What You Retrieve for Enhanced Tool Learningcited by 21
- 2026Agentic Aggregation for Parallel Scaling of Long-Horizon Agentic Taskscited by 18
- 2026TRIAGE: Role-Typed Credit Assignment for Agentic Reinforcement Learningcited by 5
- 2026ICRL: Learning to Internalize Self-Critique with Reinforcement Learningcited by 3
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