Distribution Corrected Offline Data Distillation for Large Language Models
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 o...
| Authors | Yumeng Zhang, Zhengbang Yang, Yevin Nikhel Goonatilake et al. |
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| Primary category | cs.CL |
| Published | 13 May 2026 |
| Last updated at source | 13 May 2026 |
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