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Reasoning 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.
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10/19/2026
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UnverifiedThought leakage refers to the process, during reinforcement learning training of large language models, in which the chain-of-thought content the model uses for internal reasoning leaks in unintended ways into the final output, the training signal, or externally observable interfaces.76% similarUnverifiedTraditional reasoning distillation provides the teacher model's complete reasoning trace together with the correct answer to the student model, but a reasoning trace, even when accompanied by the correct answer, does not necessarily provide effective supervision signals for a particular student.76% similarUnverifiedOutput distillation involves directly using text generated by a teacher model—such as dialogues and reasoning chains—as training data to fine-tune a student model, without requiring access to the teacher's internal parameters.75% 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.73% similarUnverifiedThe paper "Beyond the Answer: Student-Conditioned Trace Construction for Reasoning Distillation" proposes the ReDiST framework for constructing reasoning traces that improve learning effectiveness in reasoning distillation.73% similar
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