arXiv:2605.31509v1 Announce Type: new 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
This paper leverages recent advancements in large language models and reinforcement learning to address a fundamental challenge in AI agent generalization, signaling progress towards more robust autonomous systems.
Improving AI agent generalization and skill reuse is critical for developing more capable and efficient AI systems that can handle complex, real-world tasks without extensive, brittle, task-specific training.
The explicit incorporation of compression principles into RL objectives for agents introduces a new methodology for fostering more generalizable and less brittle AI behaviors.
- · AI research labs
- · Developers of autonomous systems
- · Industries deploying AI agents
- · Large language model developers
- · Companies relying on narrow, task-specific AI solutions
- · Brittle AI agent architectures
AI agents become more capable of transferring learned skills across diverse tasks, reducing development costs and improving performance.
The proliferation of more robust and autonomous AI agents accelerates automation across white-collar and specialized industrial sectors.
Increased agentic autonomy leads to significant shifts in workforce demands and the structure of professional services, potentially collapsing certain workflow layers.
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Read at arXiv cs.LG