ReuseRL 用 MDL 原则解决了智能体 RL 泛化差的痛点，做多任务智能体训练的团队可以直接参考其技能复用机制，提升模型在未见任务上的表现。
ReuseRL 是一种基于最小描述长度（MDL）原则的强化学习方法，旨在解决大语言模型智能体在强化学习中学习到脆弱、任务特定捷径的问题。该方法从成功轨迹中提取可复用的抽象技能字典，并通过分割成本惩罚编码效率低下的行为，从而鼓励智能体学习更通用、可压缩的行为模式。论文证明了该压缩惩罚的 PAC-Bayes 泛化界，并在 ALFWorld、TextWorld-Cooking 和 Countdown-Stepwise 等基准上，ReuseRL 在分布内和分布外任务上均优于 vanilla GRPO 和强基线方法。这项工作为提升智能体泛化能力提供了新思路，尤其适合需要跨任务迁移的 RL 场景。
Skill Reuse as Compression in Agentic RL
Large language model agents trained with reinforcement learning (RL) often learn brittle, task-specific shortcuts. We hypothesize that agents generalize better when their successful trajectories are structurally compressible, decomposed into a small set of reusable abstract patterns. To formalize this, we introduce ReuseRL, which grounds agentic RL in the Minimum Description Length (MDL) principle. ReuseRL extracts a shared skill dictionary from successful trajectories and augments the RL objective with a segmentation cost, explicitly penalizing idiosyncratic behaviors that encode poorly. We prove a PAC-Bayes generalization bound for this compression penalty. Across ALFWorld, TextWorld-Cooking, and Countdown-Stepwise, ReuseRL improves in- and out-of-distribution success over vanilla GRPO and strong round-length baselines.