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

1 citations · 1 across the 4 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 +4

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.LG2026

Fisher-Orthogonal Projected Natural Gradient Descent for Continual Learning

Ishir Garg, Neel Kolhe, Andy Peng +1

Continual learning aims to enable neural networks to acquire new knowledge on sequential tasks. However, the key challenge in such settings is to learn new tasks without catastroph…