Zhihao Wen

Next-turn user reactions can boost multi-turn agent performance by over 10 percentage points, revealing the critical role of local feedback in reinforcement learning.

Introducing a transport-based approach that ensures stable and expressive 3D stylizations by controlling style feature allocation across multiple views.

LLM agents learn tool use far more efficiently when their learning environment evolves alongside them, adapting to the agent's changing capabilities.