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| Автори: | , , , , |
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| Формат: | Preprint |
| Опубліковано: |
2025
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| Предмети: | |
| Онлайн доступ: | https://arxiv.org/abs/2511.00521 |
| Теги: |
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| _version_ | 1866909894867681280 |
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| author | Nguyen, Bao Nguyen, Hieu Trung She, Ruifeng Fu, Xiaojin Nguyen, Viet Anh |
| author_facet | Nguyen, Bao Nguyen, Hieu Trung She, Ruifeng Fu, Xiaojin Nguyen, Viet Anh |
| contents | Selecting an appropriate reasoning method for a given query remains a key challenge in language model generation. Existing approaches typically generate multiple candidate responses and use an aggregation strategy to select the output answer, often assuming that more candidate answers yield higher accuracy. We revisit this assumption through a rigorous theoretical analysis, deriving accuracy bounds for standard aggregation methods under fixed generation distributions and candidate sizes. Building on these insights, we introduce EPIC, an Ensemble Planning with Contrastive learning framework to learn a shared representation space that captures both model reasoning abilities and query-method compatibility. EPIC incorporates our probability bounds as a regularizer in a utility-driven optimization that balances accuracy and computational cost. Experiments on diverse mathematical reasoning tasks show that EPIC consistently selects optimal reasoning methods, improving accuracy while reducing computational overhead. Our code can be found at https://github.com/nguyenngocbaocmt02/EPIC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00521 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reasoning Planning for Language Models Nguyen, Bao Nguyen, Hieu Trung She, Ruifeng Fu, Xiaojin Nguyen, Viet Anh Machine Learning Artificial Intelligence Computation and Language Selecting an appropriate reasoning method for a given query remains a key challenge in language model generation. Existing approaches typically generate multiple candidate responses and use an aggregation strategy to select the output answer, often assuming that more candidate answers yield higher accuracy. We revisit this assumption through a rigorous theoretical analysis, deriving accuracy bounds for standard aggregation methods under fixed generation distributions and candidate sizes. Building on these insights, we introduce EPIC, an Ensemble Planning with Contrastive learning framework to learn a shared representation space that captures both model reasoning abilities and query-method compatibility. EPIC incorporates our probability bounds as a regularizer in a utility-driven optimization that balances accuracy and computational cost. Experiments on diverse mathematical reasoning tasks show that EPIC consistently selects optimal reasoning methods, improving accuracy while reducing computational overhead. Our code can be found at https://github.com/nguyenngocbaocmt02/EPIC. |
| title | Reasoning Planning for Language Models |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2511.00521 |