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cs.LG2026
Q-learning Penalized Transformer for Safe Offline Reinforcement Learning
Shengchao Hu, Peng Wang, Jifeng Hu +5
This paper addresses the problem of safe offline reinforcement learning, which involves training a policy to satisfy safety constraints using an offline dataset. This problem is in…
cs.LG2026
Write Back the : Revisiting the Same Tokens with Fresh Representations
Wencheng Ye, Anning Hu, Xiangdong Zhang +5
Transformers process information strictly forward through depth, preventing deeper computation from revisiting and refining earlier representations. To augment the standard forward…