Researchers introduce ReuseRL, a reinforcement learning framework that improves LLM agent generalization by encouraging skill reuse and compression. By grounding agentic RL in the Minimum Description Length principle and penalizing task-specific shortcuts, the method demonstrates better in- and out-of-distribution performance across multiple benchmark environments.
ReuseRL addresses a fundamental challenge in training language model agents: their tendency to overfit to specific tasks through brittle, non-generalizable shortcuts. Rather than accepting this limitation, the researchers propose that better generalization emerges when successful trajectories compress into reusable abstract skills. This insight builds on classical information theory, applying the Minimum Description Length principle—which suggests simpler, more compressible solutions generalize better—to the domain of agentic reinforcement learning.
The technical contribution centers on extracting a shared skill dictionary from successful trajectories and augmenting the RL objective with a segmentation cost that explicitly penalizes idiosyncratic behaviors. The authors provide formal grounding through a PAC-Bayes generalization bound, lending theoretical rigor to their compression-based approach. This bridges the gap between intuitive ideas about generalization and mathematical guarantees.
The methodology demonstrates measurable improvements across three diverse environments: ALFWorld (web-based tasks), TextWorld-Cooking (text-based problem solving), and Countdown-Stepwise (constraint satisfaction). Crucially, ReuseRL outperforms both vanilla GRPO baselines and round-length variants in both in-distribution and out-of-distribution scenarios, suggesting the compression principle genuinely captures something fundamental about agent generalization rather than merely fitting the training distribution differently.
For the broader AI development community, this work validates compression-based reasoning as a practical lever for improving agent robustness. As language models scale toward more complex real-world tasks, reducing reliance on task-specific brittle solutions becomes increasingly valuable. The approach could influence future directions in prompt engineering, multi-task learning architectures, and safety-critical agent deployment.
- →ReuseRL improves LLM agent generalization by encouraging compression of successful trajectories into reusable skills
- →The method applies Minimum Description Length principle to agentic RL with formal PAC-Bayes generalization bounds
- →Testing across ALFWorld, TextWorld-Cooking, and Countdown-Stepwise shows consistent improvements over strong baselines
- →Penalizing idiosyncratic task-specific behaviors reduces overfitting while maintaining in-distribution performance
- →Compression-based training could become a practical technique for deploying more robust multi-task agents