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Transformers can implement reinforcement-learning algorithms in context, yet how off-policy control emerges within Transformers remains poorly understood. We study Q-learning, a foundational model-free control algorithm that combines off-policy bootstrapping with greedy policy improvement. We prove that a two-layer att...
A unified algorithmic framework featuring two algorithms: MIN-UCB-VI for regret minimization and MAX-LCB-VI for best policy identification for best policy identification is proposed, including both instance-dependent and independent upper bounds on regret and sub-optimality gap.
This work proposes an offline-to-online combinatorial experts selection (CES) framework that leverages offline evaluation to warm-start model capability estimation and employs online learning to adapt to capability shifts and correct offline inaccuracies.
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