Wendi Li

Progress advantage reveals a powerful, annotation-free scoring mechanism that outperforms traditional reward models in LLM agentic settings.

Discovering when a robot's about to fail just got easier: Hide-and-Seek pinpoints failure signals in VLA trajectories using only coarse, trajectory-level labels, ditching the need for expensive step-by-step annotations.

Ditch reward maximization: a new RL objective learns the *distribution* of reasoning advantages, boosting LLM accuracy and diversity without extra training costs.