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Research questionHow can LLM agents generalize to unseen tasks without directly fine-tuning their policies?LLM agents can perform well on familiar tasks yet fail when they encounter unfamiliar task types. Improving this transfer is difficult because changes that help known tasks may not produce robust behavior on new ones.
AI
AI Agents
Inference Optimization
Machine Learning
Reinforcement Learning
Latest papersRecent research connected to this question, newest first.AgentRM: Enhancing Agent Generalization with Reward ModelingThe source studies AgentRM, a reward model that guides base or fine-tuned policy models during answer generation using Best-of-N sampling and step-level beam search. It examines explicit, implicit, and LLM-as-a-judge reward construction across nine tasks spanning four agent-task types, plus three held-in tasks; the evidence is limited to these reported experiments.research paper · Sep 3, 2026
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