CTPD: Cross Tokenizer Preference Distillation

2026-01-17

AI Summary

This work addresses the challenge of fine-grained preference alignment in knowledge distillation when teacher and student language models employ heterogeneous tokenizers. To overcome this, the authors propose CTPD, a unified framework that enables cross-tokenizer semantic mapping through character-level alignment. The approach introduces three key innovations: aligned span projection, Token Importance Sampling-based Direct Preference Optimization (TIS-DPO), and a teacher-anchored reference mechanism. CTPD represents the first method to achieve efficient and generalizable transfer of human preferences across disparate tokenization schemes. Extensive experiments demonstrate its significant superiority over existing approaches on multiple benchmarks, confirming its effectiveness and scalability in aligning preferences between heterogeneous language models.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Machine Learning: Learning Preferences or Rankings
• Knowledge Representation and Reasoning: Preferences

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

While knowledge distillation has seen widespread use in pre-training and instruction tuning, its application to aligning language models with human preferences remains underexplored, particularly in the more realistic cross-tokenizer setting. The incompatibility of tokenization schemes between teacher and student models has largely prevented fine-grained, white-box distillation of preference information. To address this gap, we propose Cross-Tokenizer Preference Distillation (CTPD), the first unified framework for transferring human-aligned behavior between models with heterogeneous tokenizers. CTPD introduces three key innovations: (1) Aligned Span Projection, which maps teacher and student tokens to shared character-level spans for precise supervision transfer; (2) a cross-tokenizer adaptation of Token-level Importance Sampling (TIS-DPO) for improved credit assignment; and (3) a Teacher-Anchored Reference, allowing the student to directly leverage the teacher's preferences in a DPO-style objective. Our theoretical analysis grounds CTPD in importance sampling, and experiments across multiple benchmarks confirm its effectiveness, with significant performance gains over existing methods. These results establish CTPD as a practical and general solution for preference distillation across diverse tokenization schemes, opening the door to more accessible and efficient alignment of language models.

Problem

• preference distillation
• cross-tokenizer
• language model alignment
• tokenization incompatibility
• human preference

Innovation

• Cross-Tokenizer Preference Distillation
• Aligned Span Projection
• Token-level Importance Sampling
• Teacher-Anchored Reference
• Preference Alignment