Xin Cong

Outcome-only RL can train highly effective process supervision models by rewarding accurate outcome prediction, even without explicit step-level feedback.

LLM agents are stuck in local adjustment loops, unable to adapt their training strategies despite having the resources to do so.

Tool-using agents may seem capable, but they struggle to distinguish neutral actions from errors, highlighting a critical need for better step-level process understanding.