Unverified50% confidenceFactExact time
The ReDiST workflow consists of three steps: student-draft-based trace revision, student-matched filtering and ranking, and balanced distillation loss, all completed in the offline stage, with only the distilled student model used at inference time.
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10/19/2026
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UnverifiedThe trace revision step of ReDiST starts from the student model's own drafts: for drafts with correct answers, the provided evidence is used to revise them so that their reasoning process is more rigorous; for drafts with wrong answers, the verified answer is used to provide additional guidance for repair.79% similarUnverifiedOn-policy self-distillation computes the distillation loss on trajectories sampled by the student itself, conditioning on the states produced by the student's current policy to let the teacher provide a better target distribution, thereby reducing error accumulation caused by distribution shift.71% similarVerifiedDistillation technology enables the student model to learn and reproduce the teacher's multi-step generation trajectory, compressing a generation process of hundreds of steps down to just a few.70% similarUnverifiedThrough student-conditioned construction and filtering, ReDiST offers a new approach to personalized reasoning distillation.67% similarUnverifiedReDiST uses a frozen reader model matched to the student to evaluate, based on the question, evidence, and candidate traces, their answer-recovery ability and verified-answer probability, ranking the original traces and the edited candidates.67% similar
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