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cs.LG2026
Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective
Yinan Huang, Hans Hao-Hsun Hsu, Junran Wang +2
Sequential probabilistic inference from streaming observations requires modeling distributions over future trajectories as new observations arrive. Although diffusion and flow-matc…
cs.LG2025
RLinf: Flexible and Efficient Large-scale Reinforcement Learning via Macro-to-Micro Flow Transformation
Chao Yu, Yuanqing Wang, Zhen Guo +26
Reinforcement learning (RL) has demonstrated immense potential in advancing artificial general intelligence, agentic intelligence, and embodied intelligence. However, the inherent…
cs.LG2025
Spectral Bellman Method: Unifying Representation and Exploration in RL
Ofir Nabati, Bo Dai, Shie Mannor +1
Representation learning is critical to the empirical and theoretical success of reinforcement learning. However, many existing methods are induced from model-learning aspects, misa…