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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.15242 |
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| _version_ | 1866912957050388480 |
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| author | Feng, Shengyu He, Yun Ma, Shuang Li, Beibin Xiong, Yuanhao Li, Songlin Mandyam, Karishma Katz-Samuels, Julian Bi, Shengjie Yu, Licheng Zhang, Hejia Sankararaman, Karthik Abinav Fang, Han Yang, Yiming Faruqui, Manaal |
| author_facet | Feng, Shengyu He, Yun Ma, Shuang Li, Beibin Xiong, Yuanhao Li, Songlin Mandyam, Karishma Katz-Samuels, Julian Bi, Shengjie Yu, Licheng Zhang, Hejia Sankararaman, Karthik Abinav Fang, Han Yang, Yiming Faruqui, Manaal |
| contents | Reinforcement learning (RL) has recently proven effective at scaling chain-of-thought (CoT) reasoning in large language models for tasks with verifiable answers. However, extending RL-based thought training to more general non-verifiable tasks-where supervision is provided only through pairwise human preferences-remains challenging. Existing approaches typically apply RL objectives designed for verifiable rewards to preference-based settings in a heuristic manner. In this work, we show that introducing CoT reasoning into preference modeling fundamentally changes the structure of the Bradley-Terry (BT) likelihood, as the reasoning process must be treated as a latent variable. This results in a preference likelihood expressed as a ratio of expectations over stochastic generation trajectories, which cannot be optimized using Jensen-style bounds or standard RL objectives. To address this challenge, we derive a consistent Monte Carlo estimator for the gradient of the resulting likelihood, leading to Bradley-Terry Policy Optimization (BTPO). Empirically, BTPO enables stable and effective training of generative preference models with CoT reasoning, consistently outperforming prior heuristic approaches across multiple benchmarks and model scales. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15242 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bradley-Terry Policy Optimization for Generative Preference Modeling Feng, Shengyu He, Yun Ma, Shuang Li, Beibin Xiong, Yuanhao Li, Songlin Mandyam, Karishma Katz-Samuels, Julian Bi, Shengjie Yu, Licheng Zhang, Hejia Sankararaman, Karthik Abinav Fang, Han Yang, Yiming Faruqui, Manaal Machine Learning Reinforcement learning (RL) has recently proven effective at scaling chain-of-thought (CoT) reasoning in large language models for tasks with verifiable answers. However, extending RL-based thought training to more general non-verifiable tasks-where supervision is provided only through pairwise human preferences-remains challenging. Existing approaches typically apply RL objectives designed for verifiable rewards to preference-based settings in a heuristic manner. In this work, we show that introducing CoT reasoning into preference modeling fundamentally changes the structure of the Bradley-Terry (BT) likelihood, as the reasoning process must be treated as a latent variable. This results in a preference likelihood expressed as a ratio of expectations over stochastic generation trajectories, which cannot be optimized using Jensen-style bounds or standard RL objectives. To address this challenge, we derive a consistent Monte Carlo estimator for the gradient of the resulting likelihood, leading to Bradley-Terry Policy Optimization (BTPO). Empirically, BTPO enables stable and effective training of generative preference models with CoT reasoning, consistently outperforming prior heuristic approaches across multiple benchmarks and model scales. |
| title | Bradley-Terry Policy Optimization for Generative Preference Modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.15242 |