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20242026
most citedEfficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data

1 citations · 2 across the 8 of their papers we have counts for

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cs.LG2026

Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning

Zhiyuan Zhou, Andy Peng, Charles Xu +5

Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control.…

cs.CV2026

Any to Full: Prompting Depth Anything for Depth Completion in One Stage

Zhiyuan Zhou, Ruofeng Liu, Taichi Liu +4

Accurate, dense depth estimation is crucial for robotic perception, but commodity sensors often yield sparse or incomplete measurements due to hardware limitations. Existing RGBD-f…