TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling

📄 arXiv: 2508.17445v1 📥 PDF

作者: Yizhi Li, Qingshui Gu, Zhoufutu Wen, Ziniu Li, Tianshun Xing, Shuyue Guo, Tianyu Zheng, Xin Zhou, Xingwei Qu, Wangchunshu Zhou, Zheng Zhang, Wei Shen, Qian Liu, Chenghua Lin, Jian Yang, Ge Zhang, Wenhao Huang

分类: cs.LG, cs.CL

发布日期: 2025-08-24

💡 一句话要点

提出TreePO以解决强化学习推理效率与效果之间的矛盾

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 强化学习 推理效率 树结构模型 动态采样 段级优势估计 自然语言处理 计算资源优化

📋 核心要点

  1. 现有方法在强化学习推理中面临昂贵的在线回放和有限的多样性探索问题。
  2. 论文提出TreePO，通过树结构搜索和动态采样策略来优化推理效率与效果。
  3. 实验结果表明，TreePO在推理基准上显著提升性能，并有效减少计算资源消耗。

📝 摘要（中文）

近年来，通过强化学习对大型语言模型进行对齐的进展在解决复杂推理问题上取得了显著成效，但代价是昂贵的在线策略回放和有限的多样性推理路径探索。本文提出TreePO，采用自导向回放算法将序列生成视为树结构搜索过程。TreePO通过动态树采样策略和固定长度段解码，利用局部不确定性来生成额外分支。通过在公共前缀上摊销计算并提前修剪低价值路径，TreePO有效降低了每次更新的计算负担，同时保持或增强了探索的多样性。关键贡献包括：段级采样算法、树结构段级优势估计以及动态发散和回退策略的有效性分析。我们在一系列推理基准上验证了TreePO的性能提升，并显示出GPU小时节省22%至43%的效率，同时在轨迹级和令牌级采样计算上分别减少了40%和35%。

🔬 方法详解

问题定义：本文旨在解决现有强化学习方法在推理效率和效果之间的矛盾，尤其是昂贵的在线策略回放和有限的推理路径探索带来的挑战。

核心思路：TreePO的核心思想是将序列生成视为树结构的搜索过程，通过动态树采样和固定长度段解码来优化计算效率，同时增强探索的多样性。

技术框架：TreePO的整体架构包括动态树采样策略、固定长度段解码和局部不确定性引导的分支生成。通过在公共前缀上摊销计算，提前修剪低价值路径，降低计算负担。

关键创新：TreePO的主要创新在于段级采样算法和树结构段级优势估计，这与现有方法的线性采样和全局优化策略形成了本质区别。

关键设计：在设计中，TreePO采用了段级采样机制以减轻KV缓存负担，并引入了早停机制和动态发散策略，以提高推理效率和效果。

📊 实验亮点

实验结果显示，TreePO在推理基准上实现了22%至43%的GPU小时节省，同时在轨迹级和令牌级采样计算上分别减少了40%和35%。这些结果表明TreePO在提升推理效率的同时，保持了或增强了模型的推理效果，展现了其在实际应用中的巨大潜力。

🎯 应用场景

TreePO的研究成果在自然语言处理、对话系统和智能问答等领域具有广泛的应用潜力。通过提高推理效率和效果，TreePO能够支持更复杂的任务处理，降低计算资源的需求，推动强化学习在实际应用中的落地。未来，TreePO可能会在大规模模型训练和推理中发挥更大作用，促进智能系统的进一步发展。

📄 摘要（原文）

Recent advancements in aligning large language models via reinforcement learning have achieved remarkable gains in solving complex reasoning problems, but at the cost of expensive on-policy rollouts and limited exploration of diverse reasoning paths. In this work, we introduce TreePO, involving a self-guided rollout algorithm that views sequence generation as a tree-structured searching process. Composed of dynamic tree sampling policy and fixed-length segment decoding, TreePO leverages local uncertainty to warrant additional branches. By amortizing computation across common prefixes and pruning low-value paths early, TreePO essentially reduces the per-update compute burden while preserving or enhancing exploration diversity. Key contributions include: (1) a segment-wise sampling algorithm that alleviates the KV cache burden through contiguous segments and spawns new branches along with an early-stop mechanism; (2) a tree-based segment-level advantage estimation that considers both global and local proximal policy optimization. and (3) analysis on the effectiveness of probability and quality-driven dynamic divergence and fallback strategy. We empirically validate the performance gain of TreePO on a set reasoning benchmarks and the efficiency saving of GPU hours from 22\% up to 43\% of the sampling design for the trained models, meanwhile showing up to 40\% reduction at trajectory-level and 35\% at token-level sampling compute for the existing models. While offering a free lunch of inference efficiency, TreePO reveals a practical path toward scaling RL-based post-training with fewer samples and less compute. Home page locates at https://m-a-p.ai/TreePO.