Author

Hechang Chen

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#artificial intelligence Preprint Oct 2026

From Suboptimal to Optimal: One-Step Latent Flow Policies for Offline Reinforcement Learning

Offline reinforcement learning aims to learn effective policies from fixed datasets without environment interaction. Although generative models such as diffusion and flow matching enhance policy expressiveness, they typically start from uninformative Gaussian noise and generate actions through multi-step iteration, lea...

Sheng Lu, Zi-Xuan Yang, Rong-Xu Zhao et al. · 0 citations

Open access Sep 2026

scRep: A Latent-Space Self-Distilled Foundation Model for Single-Cell Representation Learning

Single-cell foundation models have shown strong potential for learning transferable representations from large-scale transcriptomic data. However, many existing approaches rely on reconstructing masked gene expression values, creating a potential mismatch between observation-space reconstruction and the goal of learnin...

Sheng-Jie Wang, Zong-Yong Hu, Yunlong Bie et al. · bioRxiv · 0 citations