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This research proposes ReuseRL, a reinforcement learning framework that enhances LLM agent generalization by enforcing structural compressibility through the Minimum Description Length principle. By extracting a shared skill dictionary from successful trajectories and penalizing idiosyncratic behaviors, the method effectively mitigates brittle, task-specific shortcuts common in current agentic RL systems. The approach demonstrates improved performance across multiple benchmarks by grounding agent behavior in reusable abstract patterns rather than memorized sequences.
Skill Reuse as Compression in Agentic RL
arXiv:2605.31509v2 Announce Type: replace Abstract: 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 bound guaranteeing that a dictionary extracted from successful trajectories has bounded expected description length on future successful behavior. Across ALFWorld, TextWorld-Cooking, and Countdown-Stepwise, ReuseRL improves in- and out-of-distribution success over vanilla GRPO and strong round-length baselines.
Abstract: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 bound guaranteeing that a dictionary extracted from successful trajectories has bounded expected description length on future successful behavior. Across ALFWorld, TextWorld-Cooking, and Countdown-Stepwise, ReuseRL improves in- and out-of-distribution success over vanilla GRPO and strong round-length baselines.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2605.31509 [cs.LG]
(or arXiv:2605.31509v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.31509 Focus to learn more arXiv-issued DOI via DataCite
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