Advantage-Guided Distillation for Preference Alignment in Small Language Models
Keywords: Preference Alignment; Large language model; Knowledge Distillation; Advantage Function
Abstract: Alignment techniques such as RLHF enable LLMs to generate outputs that align with human preferences and play an essential role in their effectiveness. However, their impact often diminishes when applied to smaller language models, likely due to the limited capacity of these models. Instead of directly applying existing alignment techniques to smaller models, we propose to utilize a well-aligned teacher LLM to guide the alignment process for these models, thereby facilitating the transfer of the teacher's knowledge of human preferences to the student model. To achieve this, we first explore a straightforward approach, Dual-Constrained Knowledge Distillation (DCKD), that employs knowledge distillation with two KL-divergence constraints from the aligned teacher to the unaligned student. To further enhance the contrastive effect, we then propose Advantage-Guided Distillation for Preference Alignment (ADPA), which leverages an advantage function from the aligned teacher to deliver more nuanced, distribution-level reward signals for the student's alignment. Our experimental results demonstrate that these two approaches appreciably improve the alignment of smaller language models and narrow the performance gap with their larger counterparts.
Supplementary Material: zip
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 7508
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