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cs.LG2026
QAMM: Adjoint MeanFlow Matching for Few-Step Offline Reinforcement Learning
Yuehu Gong, Shutong Ding, Mokai Pan +4
Flow policies can model rich action distributions, but their iterative sampling limits decision speed. Adjoint matching uses the critic's action gradient to improve a flow policy w…
cs.LG2026
Distributional Reinforcement Learning with Diffusion Bridge Critics
Shutong Ding, Yimiao Zhou, Ke Hu +5
Recent advances in diffusion-based reinforcement learning (RL) methods have demonstrated promising results in a wide range of continuous control tasks. However, existing works in t…