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From Weak Data to Strong Policy: Q-Targets Enable Provable In-Context Reinforcement Learning
Existing in-context reinforcement learning methods mainly pretrain Transformers with supervised behavior-prediction objectives. This enables task inference from context, but makes the learned policy strongly depend on the quality of offline actions: when trajectories are weak or suboptimal, imitatio
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