Unverified50% confidenceFactExact time
The endpoint-style policy distillation method uses the difference in log probabilities of the teacher model before and after RL training as the supervision signal, applied to trajectories generated by the student model itself.
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10/16/2026
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UnverifiedIn LLM post-training, the model directly spews intermediate reasoning steps that should have been hidden to the user, or during RL reward computation, the reward model that should only evaluate the final answer sees the reasoning process, thereby distorting the optimization direction.80% similarUnverifiedIn confidence-gated distillation, the distillation signal for a sample is only incorporated into training when the teacher model's prediction confidence for that sample exceeds a threshold.78% similarVerifiedThis experiment adopts trajectory-level distillation, which belongs to the category of behavioral cloning, directly having the student model imitate the external behavioral sequences of the teacher model.78% similarVerifiedIn offline distillation, training data is generated once by the teacher model and fixed, with the student model learning from a static dataset.76% similarUnverifiedIn on-policy distillation, the student model first generates trajectories itself, and then the teacher model provides supervision signals for these paths, thereby avoiding the distribution shift between offline data and student behavior.75% similar
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