Transformers Provably Implement In-Context Reinforcement Learning with Policy Improvement
概要
arXiv:2605.05755v1 Announce Type: cross Abstract: We investigate the ability of transformers to perform in-context reinforcement learning (ICRL), where a model must infer and execute learning algorithms from trajectory data without parameter updates. We show that a linear self-attention transformer…
