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Learning to Act with Task Progress: Distilling Small Agents from Compact Teacher Supervision
Learning from large-model demonstrations offers a way to train small agents that can complete recurring tasks without calling a large model at every step. A central design choice is what to retain from teacher trajectories that contain reasoning, actions, and information about task progress. We intr
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