[2605.14071] Distribution Corrected Offline Data Distillation for Large Language Models
[Submitted on 13 May 2026]
Title:Distribution Corrected Offline Data Distillation for Large Language Models
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Abstract:Distilling reasoning traces from strong large language models into smaller ones is a promising route to improve intelligence in resource-constrained settings. Existing approaches face a fundamental trade-off: offline distillation from teacher-generated traces provides high-quality, sample-efficient supervision but suffers from distributional drift: during training, the student model conditions on teacher-generated prefixes, whereas during inference the student autoregresses on self-generated prefixes, leading to compounding errors over long reasoning trajectories. Meanwhile, on-policy or self-distillation methods better match the student's inference-time distribution, but require costly online sampling and often produce low-quality traces in early training. We propose a principled offline reasoning distillation framework that preserves the efficiency and supervision quality of offline teacher-generated data while correcting teacher-student distribution drift. It adaptively emphasizes teacher supervision that is better aligned with the student's on-policy distribution. Evaluations on mathematical reasoning benchmarks of GSM8K, MATH, MATH500, and harder held-out competition-style tasks, including AMC, AIME, and OlympiadBench, show that our method improves reasoning accuracy over prior offline distillation algorithms and yields more stable reasoning traces while preserving instruction-following capabilities. Our work shows that lightweight, distribution-correction-aware training can substantially strengthen offline reasoning distillation without online rollouts.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2605.14071 [cs.CL]
(or arXiv:2605.14071v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.14071
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yumeng Zhang [view email] [v1] Wed, 13 May 2026 19:47:31 UTC (1,102 KB)
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