ICLR Paper: Injecting Distractors Improves Cross-Domain Generalization in LLM Agents
zhaoran_wang · x · 2026-07-27
A research paper on Reinforcement Learning (RL) for LLM agents explores how to reduce the generalization tax in cross-domain scenarios.
Key Findings:
- Cross-domain generalization is driven more by state information richness and planning complexity than by domain realism or text similarity.
- The study shows that simply injecting lightweight, task-irrelevant distractors into the state during RL training can significantly improve out-of-distribution (OOD) robustness.
Related event: ICLR Paper: State Information Dictates LLM Agent Generalization(2 posts)→
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