AgentRM: Enhancing Agent Generalization with Reward Modeling
Quick summary
arXiv:2502.18407v2 Announce Type: replace-cross Abstract: Existing LLM-based agents have achieved strong performance on held-in tasks, but their generalizability to unseen tasks remains poor. Hence, some recent work focus on fine-tuning the policy model with more diverse tasks to improve the generalizability. In this work, we find that finetuning a reward model to guide the policy model is more robust than directly finetuning the policy model. Based on this finding, we propose AgentRM, a generalizable reward model, to guide the policy model for effective test-time search. We comprehensively in
Key takeaways
- arXiv:2502.18407v2 Announce Type: replace-cross Abstract: Existing LLM-based agents have achieved strong performance on held-in tasks, but their generalizability to unseen tasks remains poor.
- Hence, some recent work focus on fine-tuning the policy model with more diverse tasks to improve the generalizability.
- In this work, we find that finetuning a reward model to guide the policy model is more robust than directly finetuning the policy model.
Why it matters
“AgentRM: Enhancing Agent Generalization with Reward Modeling” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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