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Long-term LLM assistants must repeatedly decide what prior context to expose and which model should answer as an interaction evolves. Existing memory systems and model routers usually make these decisions separately, although memory construction changes the evidence, distractors, and difficulty of the resulting input....
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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