On the Step Length Confounding in LLM Reasoning Data Selection
Large reasoning models have recently demonstrated strong performance on complex tasks that require long chain-of-thought reasoning, through supervised fine-tuning on large-scale and high-quality datasets. To construct such datasets, existing pipelines generate long reasoning data from more capable Large Language Models (LLMs) and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. Despite the proven effectiveness of naturalness-based data selection, which ranks data by the average log probability assigned by LLMs, our analysis shows that, when applied to LLM reasoning datasets, it systematically prefers samples with longer reasoning steps (i.e., more tokens per step) rather than higher-quality ones, a phenomenon we term step length confounding. Through quantitative analysis, we attribute this phenomenon to low-probability first tokens in reasoning steps; longer steps dilute their influence, thereby inflating the average log probabilities. To address this issue, we propose two variant methods: ASLEC-DROP, which drops first-token probabilities when computing average log probability, and ASLEC-CASL, which applies a causal debiasing regression to remove the first tokens' confounding effect. Experiments across four LLMs and five evaluation benchmarks demonstrate the effectiveness of our approach in mitigating the step length confounding problem.
69d849dd79548974da9463f5
Bing Wang
claimed_verified
2026-04-10T07:45:07.270Z
6225b0d87f5fba1007d62fae
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Bing Wang
wangbing1416
user
wangbing1416
69d849dd79548974da9463f6
Rui Miao
69d849dd79548974da9463f7
Chen Shen
69d849dd79548974da9463f8
Shaotian Yan
69d849dd79548974da9463f9
Kaiyuan Liu
69d849dd79548974da9463fa
Ximing Li
69d849dd79548974da9463fb
Xiaosong Yuan
69d849dd79548974da9463fc
Sinan Fan
69d849dd79548974da9463fd
Jun Zhang
69d849dd79548974da9463fe
Jieping Ye
On the Step Length Confounding in LLM Reasoning Data Selection
Large reasoning models have recently demonstrated strong performance on complex tasks that require long chain-of-thought reasoning, through supervised fine-tuning on large-scale and high-quality datasets. To construct such datasets, existing pipelines generate long reasoning data from more capable Large Language Models (LLMs) and apply manually heuristic or naturalness-based selection methods to filter high-quality samples. Despite the proven effectiveness of naturalness-based data selection, which ranks data by the average log probability assigned by LLMs, our analysis shows that, when applied to LLM reasoning datasets, it systematically prefers samples with longer reasoning steps (i.e., more tokens per step) rather than higher-quality ones, a phenomenon we term step length confounding. Through quantitative analysis, we attribute this phenomenon to low-probability first tokens in reasoning steps; longer steps dilute their influence, thereby inflating the average log probabilities. To address this issue, we propose two variant methods: ASLEC-DROP, which drops first-token probabilities when computing average log probability, and ASLEC-CASL, which applies a causal debiasing regression to remove the first tokens' confounding effect. Experiments across four LLMs and five evaluation benchmarks demonstrate the effectiveness of our approach in mitigating the step length confounding problem.
Researchers identified a bias in naturalness-based data selection for reasoning tasks where longer reasoning steps are preferred over higher-quality ones, and proposed two debiasing methods to improve dataset quality.
Large Language Models supervised fine-tuning reasoning steps log probability causal debiasing regression first-token probabilities step length confounding