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arXiv · 2509.25100

ORPO-Distill: Mixed-Policy Preference Optimization for Cross-Architecture LLM Distillation

Aasheesh Singh·Vishal Vaddina·Dagnachew Birru
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Abstract

We introduce ORPO-Distill, a general-purpose method for cross-architecture LLM distillation that formulates the problem as a preference optimization task. Unlike standard CoT distillation, the approach transfers knowledge through diverse reasoning traces. It employs an Odds-Ratio Preference Optimization objective that contrasts teacher and student traces for more effective learning, and adopts a mixed-policy strategy for utilizing student-generated outputs, outperforming both off- and on-policy alternatives. Experiments on five datasets and multiple student models show consistent improvements over conventional black-box KD baselines.

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Aasheesh Singh, Vishal Vaddina, Dagnachew Birru. 2025-09-29. ORPO-Distill: Mixed-Policy Preference Optimization for Cross-Architecture LLM Distillation. https://arxiv.org/abs/2509.25100

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