APPO: Agentic Procedural Policy Optimization
2026-06-10 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors look at how AI agents learn to make decisions when using tools over multiple steps. They found that important decisions happen throughout the process, not just when tools are used, and that simple measures of uncertainty don't always show which decisions matter most. To fix this, they created a new method called APPO that picks better points to explore and improves how rewards are assigned for decisions. Their tests show APPO helps AI agents perform better on many tasks while still using tools efficiently and staying understandable.
Reinforcement Learninglarge language modelsagentic RLcredit assignmenttool usepolicy optimizationbranchingtoken entropyadvantage scalingmulti-turn decision making
Authors
Xucong Wang, Ziyu Ma, Yong Wang, Yuxiang Ji, Shidong Yang, Guanhua Chen, Pengkun Wang, Xiangxiang Chu
Abstract
Recent advances in agentic Reinforcement Learning (RL) have substantially improved the multi-turn tool-use capabilities of large language model agents. However, most existing methods assign credit over coarse heuristic units, such as tool-call boundaries or fixed workflows, making it difficult to identify which intermediate decisions influence downstream outcomes. In this work, we study agentic RL from two perspectives: \textit{where to branch and how to assign credit after branching}. Our pilot analysis shows that influential decision points are broadly distributed throughout the generated sequence rather than concentrated at tool calls, while token entropy alone does not reliably reflect their impact on final outcomes. Motivated by these observations, we propose \textbf{Agentic Procedural Policy Optimization (APPO)}, which shifts branching and credit assignment from coarse interaction units to fine-grained decision points in the sequence. APPO selects branching locations using a Branching Score that combines token uncertainty with policy-induced likelihood gains of subsequent continuations, enabling more targeted exploration while filtering out spurious high-entropy positions. It further introduces procedure-level advantage scaling to better distribute credit across branched rollouts. Experiments on 13 benchmarks show that APPO consistently improves strong agentic RL baselines by nearly 4 points, while keeping efficient tool-calls and maintaining behavior interpretability.