Skill Reuse as Compression in Agentic RL
Quick summary
arXiv:2605.31509v2 Announce Type: replace-cross 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 p
Key takeaways
- arXiv:2605.31509v2 Announce Type: replace-cross 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.
Why it matters
This model development creates a new option for users and a new testing obligation for developers. A fixed evaluation set comparing quality, cost and failure behavior is more useful than launch claims.
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