AgentRM: Enhancing Agent Generalization with Reward Modeling

Understand

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.