做AI智能体强化学习的团队终于有了更精细的信用分配方法——APPO在13个基准上稳定提升4个点，且不牺牲效率，做多轮工具调用优化的开发者值得一试。
APPO（Agentic Procedural Policy Optimization）是一种新的强化学习方法，旨在改进大语言模型智能体的多轮工具使用能力。传统方法在粗粒度的交互单元（如工具调用边界）上分配信用，难以识别影响最终结果的关键中间决策。APPO通过分支评分（Branching Score）在序列中细粒度地选择分支点，并结合程序级优势缩放（procedure-level advantage scaling）更精确地分配信用。实验表明，APPO在13个基准测试上平均提升近4个百分点，同时保持高效的工具调用和可解释性。这项研究解决了智能体强化学习中信用分配不精确的问题，对开发更可靠、高效的AI智能体具有重要意义。
APPO: Agentic Procedural Policy Optimization
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.