Influence Functions for Efficient Data Selection in Reasoning

• URL: http://arxiv.org/abs/2510.06108v1
• Date: Tue, 07 Oct 2025 16:40:42 GMT
• Title: Influence Functions for Efficient Data Selection in Reasoning
• Authors: Prateek Humane, Paolo Cudrano, Daniel Z. Kaplan, Matteo Matteucci, Supriyo Chakraborty, Irina Rish,
• Abstract summary: Fine-tuning large language models (LLMs) on chain-of-thought (CoT) data shows that a small amount of high-quality data can outperform massive datasets.<n>We propose to define reasoning data quality using influence functions, which measure the causal effect of individual CoT examples on downstream accuracy.
• Score: 22.94556593981994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fine-tuning large language models (LLMs) on chain-of-thought (CoT) data shows that a small amount of high-quality data can outperform massive datasets. Yet, what constitutes "quality" remains ill-defined. Existing reasoning methods rely on indirect heuristics such as problem difficulty or trace length, while instruction-tuning has explored a broader range of automated selection strategies, but rarely in the context of reasoning. We propose to define reasoning data quality using influence functions, which measure the causal effect of individual CoT examples on downstream accuracy, and introduce influence-based pruning, which consistently outperforms perplexity and embedding-based baselines on math reasoning within a model family.

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