Unverified50% confidenceFactExact time
Traditional 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.
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10/19/2026
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VerifiedResearch has found that the chain-of-thought reasoning generated by models does not fully correspond to their actual internal computation processes, and may provide post-hoc explanations unrelated to the true decision-making path.76% 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.76% similarUnverifiedThe challenge of streaming model distillation lies in the fact that if the teacher model adopts a non-causal architecture, its latent representations contain future information that the student model cannot access during inference, requiring the design of alignment strategies to prevent the student from being forced to fit information it cannot reproduce.74% similarUnverifiedMainstream imitation learning methods are built on the assumption of learning only from perfect reasoning trajectories, such that the model never sees how to adjust midway through an error.74% similarUnverifiedTeacher forcing introduces the exposure bias problem: during training the real target token is fed as input, while during inference the model's own predicted token is used, and prediction errors accumulate and amplify in subsequent steps.73% similar
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