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Policy distillation is a technique that compresses the knowledge of multiple expert policies into a single student policy, usually implemented by minimizing the KL divergence or cross-entropy between the student network and expert outputs.
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10/26/2026
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UnverifiedPolicy distillation typically refers to using a student policy to imitate the behavior of one or more teacher policies.79% similarUnverifiedReasoning distillation is a branch of knowledge distillation, referring to using reasoning processes generated by a large teacher model (rather than only the final answer) to train a smaller student model so that it acquires step-by-step reasoning ability.70% similarUnverifiedCommon technical approaches for efficiency-oriented models include knowledge distillation, sparse activation (MoE architectures activate only a subset of expert networks), and task-specific fine-tuning.70% similarUnverifiedModel distillation refers to the process of querying a target model extensively, collecting its input-output pairs, and then training a student model with comparable capabilities.70% 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.69% similar
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