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Experiments on synthetic decision tasks, open bandit data, and financial rebalancing show that PW-OCP and DR-OCP improve counterfactual coverage and downstream regret without sacrificing prediction-set sharpness.
Q-Target Pretrained Transformers is proposed, which keeps the context-conditioned Transformer architecture but replaces behavior cloning with a Bellman-style Q-target objective, and learns to use rewards and transitions in the context to estimate action values, rather than simply imitating the behavior policy.
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