RL study finds LLM agents generalize better with richer state information than realistic tasks
Graham_dePenros · x · 2026-07-27
Cross-domain generalization in RL for LLM agents depends more on state richness than realism
The post highlights a paper, “Paying Less Generalization Tax: A Cross-Domain Generalization Study of RL Training for LLM Agents.” Its main takeaway is that out-of-domain generalization is driven more by how much information is in the state and how complex the planning problem is than by domain realism or text similarity.
- The paper argues that adding lightweight, task-irrelevant distractors during RL training can improve robustness.
- The author suggests it is worth reading for anyone working on LLM agents, RL, or generalization.
Related event: ICLR Paper: State Information Dictates LLM Agent Generalization(2 posts)→
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