Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning

TL;DR

This paper introduces a token-level data cleaning method for supervised fine-tuning of large language models, which filters uninformative tokens to enhance downstream task performance.

Contribution

It proposes a novel token quality evaluation and filtering pipeline that improves fine-tuning effectiveness by focusing on token-level data selection.

Findings

Token cleaning improves downstream performance.

Single-pass and iterative methods are both effective.

Theoretical analysis of error bounds supports the approach.

Abstract

Recent studies show that in supervised fine-tuning (SFT) of large language models (LLMs), data quality matters more than quantity. While most data cleaning methods concentrate on filtering entire samples, the quality of individual tokens within a sample can vary significantly. After pre-training, even in high-quality samples, patterns or phrases that are not task-related can be redundant, uninformative, or even harmful. Continuing to fine-tune on these patterns may offer limited benefit and even degrade downstream task performance. In this paper, we investigate token quality from a noisy-label perspective and propose a generic token cleaning pipeline for SFT tasks. Our method filters out uninformative tokens while preserving those carrying key task-specific information. Specifically, we first evaluate token quality by examining the influence of model updates on each token, then apply a threshold-based separation. The token influence can be measured in a single pass with a fixed reference model or iteratively with self-evolving reference models. The benefits and limitations of both methods are analyzed theoretically by error upper bounds. Extensive experiments show that our framework consistently improves downstream performance. Code is available at https://github.com/UCSC-REAL/TokenCleaning.