Skill Reuse as Compression in Agentic RLLarge 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.EMNLP 2026 · May 2026Zhikun Xu, Yu Feng, Jacob Dineen, Taiwei Shi, Jieyu Zhao, Ben Zhou