Skill Reuse as Compression in Agentic RL
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| Authors | Zhikun Xu et al. |
| Year | 2026 |
| Field | Machine Learning |
| arXiv | 2605.31509 |
| Download | |
| Categories | cs.LG, cs.AI |
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 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.
Engineering Breakdown
The Problem
Large language model agents trained with reinforcement learning (RL) often learn brittle, task-specific shortcuts.
The Approach
To formalize this, we introduce ReuseRL, which grounds agentic RL in the Minimum Description Length (MDL) principle.
Key Results
Across ALFWorld, TextWorld-Cooking, and Countdown-Stepwise, ReuseRL improves in- and out-of-distribution success over vanilla GRPO and strong round-length baselines.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Model training
- Generalization
- Optimization
- Supervised learning
- Deep learning
- Compression
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