GenSelect: A Generative Approach to Best-of-N
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909703102005248 |
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| author | Toshniwal, Shubham Sorokin, Ivan Ficek, Aleksander Moshkov, Ivan Gitman, Igor |
| author_facet | Toshniwal, Shubham Sorokin, Ivan Ficek, Aleksander Moshkov, Ivan Gitman, Igor |
| contents | Generative reward models with parallel sampling have enabled effective test-time scaling for reasoning tasks. Current approaches employ pointwise scoring of individual solutions or pairwise comparisons. However, pointwise methods underutilize LLMs' comparative abilities, while pairwise methods scale inefficiently with larger sampling budgets. We introduce GenSelect, where the LLM uses long reasoning to select the best solution among N candidates. This leverages LLMs' comparative strengths while scaling efficiently across parallel sampling budgets. For math reasoning, we demonstrate that reasoning models, such as QwQ and DeepSeek-R1-0528, excel at GenSelect, outperforming existing scoring approaches with simple prompting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GenSelect: A Generative Approach to Best-of-N Toshniwal, Shubham Sorokin, Ivan Ficek, Aleksander Moshkov, Ivan Gitman, Igor Machine Learning Computation and Language Generative reward models with parallel sampling have enabled effective test-time scaling for reasoning tasks. Current approaches employ pointwise scoring of individual solutions or pairwise comparisons. However, pointwise methods underutilize LLMs' comparative abilities, while pairwise methods scale inefficiently with larger sampling budgets. We introduce GenSelect, where the LLM uses long reasoning to select the best solution among N candidates. This leverages LLMs' comparative strengths while scaling efficiently across parallel sampling budgets. For math reasoning, we demonstrate that reasoning models, such as QwQ and DeepSeek-R1-0528, excel at GenSelect, outperforming existing scoring approaches with simple prompting. |
| title | GenSelect: A Generative Approach to Best-of-N |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2507.17797 |