CC-LEARN: Cohort-based Consistency Learning
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909652619362304 |
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| author | Ye, Xiao Shrivastava, Shaswat Li, Zhaonan Dineen, Jacob Lu, Shijie Ahuja, Avneet Shen, Ming Xu, Zhikun Zhou, Ben |
| author_facet | Ye, Xiao Shrivastava, Shaswat Li, Zhaonan Dineen, Jacob Lu, Shijie Ahuja, Avneet Shen, Ming Xu, Zhikun Zhou, Ben |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15662 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CC-LEARN: Cohort-based Consistency Learning Ye, Xiao Shrivastava, Shaswat Li, Zhaonan Dineen, Jacob Lu, Shijie Ahuja, Avneet Shen, Ming Xu, Zhikun Zhou, Ben Computation and Language 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. |
| title | CC-LEARN: Cohort-based Consistency Learning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.15662 |