Online Knowledge Distillation with Reward Guidance

Chen Jia

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arXiv · 2505.18952

Online Knowledge Distillation with Reward Guidance

Chen Jia

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Abstract

This work studies knowledge distillation (KD) for large language models (LLMs) through preference optimization. We propose a reward-guided imitation learning framework for sequential KD, formulating a min-max optimization problem between the policy and reward model (RM) to minimize the performance gap between the student and teacher policies. Specifically, the reward optimization is constrained to achieve near-optimality within a confidence set for preference alignment. For preference data construction, we explore both offline and online preference-based KD. Additionally, we reformulate the RM using the $Q$-value function and extend the framework to white-box KD, where the teacher policy's predicted probabilities are accessible. Theoretical analysis and empirical results demonstrate the effectiveness of the proposed framework.

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Chen Jia. 2025-05-25. Online Knowledge Distillation with Reward Guidance. https://arxiv.org/abs/2505.18952

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