CC-LEARN: Cohort-based Consistency Learning

• URL: http://arxiv.org/abs/2506.15662v1
• Date: Wed, 18 Jun 2025 17:41:28 GMT
• Title: CC-LEARN: Cohort-based Consistency Learning
• Authors: Xiao Ye, Shaswat Shrivastava, Zhaonan Li, Jacob Dineen, Shijie Lu, Avneet Ahuja, Ming Shen, Zhikun Xu, Ben Zhou,
• Abstract summary: Large language models struggle with consistent, robust reasoning.<n>We introduce cohort-based Consistency Learning (CC-Learn)<n>Experiments show that CC-Learn boosts both accuracy and reasoning stability over pretrained and SFT baselines.
• Score: 5.7716971260066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models excel at many tasks but still struggle with consistent, robust reasoning. We introduce Cohort-based Consistency Learning (CC-Learn), a reinforcement learning framework that improves the reliability of LLM reasoning by training on cohorts of similar questions derived from shared programmatic abstractions. To enforce cohort-level consistency, we define a composite objective combining cohort accuracy, a retrieval bonus for effective problem decomposition, and a rejection penalty for trivial or invalid lookups that reinforcement learning can directly optimize, unlike supervised fine-tuning. Optimizing this reward guides the model to adopt uniform reasoning patterns across all cohort members. Experiments on challenging reasoning benchmarks (including ARC-Challenge and StrategyQA) show that CC-Learn boosts both accuracy and reasoning stability over pretrained and SFT baselines. These results demonstrate that cohort-level RL effectively enhances reasoning consistency in LLMs.

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