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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2504.07527 |
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| _version_ | 1866918040911740928 |
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| author | Zhang, Junjie Yang, Rushuai Liu, Shunyu Lin, Ting-En Huang, Fei Chen, Yi Li, Yongbin Tao, Dacheng |
| author_facet | Zhang, Junjie Yang, Rushuai Liu, Shunyu Lin, Ting-En Huang, Fei Chen, Yi Li, Yongbin Tao, Dacheng |
| contents | In this work, we establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning under the token-level Markov decision process, revealing that large language models indeed learn an implicit $Q$-function for inference. Through this theoretical lens, we demonstrate that the widely used beam search method suffers from unacceptable over-optimism, where inference errors are inevitably amplified due to inflated $Q$-value estimations of suboptimal steps. To address this limitation, we propose Supervised Optimism Correction(SOC), which introduces a simple yet effective auxiliary loss for token-level $Q$-value estimations during supervised fine-tuning. Specifically, the auxiliary loss employs implicit value regularization to boost model confidence in expert-demonstrated responses, thereby suppressing over-optimism toward insufficiently supervised responses. Extensive experiments on mathematical reasoning benchmarks, including GSM8K, MATH, and GAOKAO, showcase the superiority of the proposed SOC with beam search across a series of open-source models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07527 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Supervised Optimism Correction: Be Confident When LLMs Are Sure Zhang, Junjie Yang, Rushuai Liu, Shunyu Lin, Ting-En Huang, Fei Chen, Yi Li, Yongbin Tao, Dacheng Computation and Language In this work, we establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning under the token-level Markov decision process, revealing that large language models indeed learn an implicit $Q$-function for inference. Through this theoretical lens, we demonstrate that the widely used beam search method suffers from unacceptable over-optimism, where inference errors are inevitably amplified due to inflated $Q$-value estimations of suboptimal steps. To address this limitation, we propose Supervised Optimism Correction(SOC), which introduces a simple yet effective auxiliary loss for token-level $Q$-value estimations during supervised fine-tuning. Specifically, the auxiliary loss employs implicit value regularization to boost model confidence in expert-demonstrated responses, thereby suppressing over-optimism toward insufficiently supervised responses. Extensive experiments on mathematical reasoning benchmarks, including GSM8K, MATH, and GAOKAO, showcase the superiority of the proposed SOC with beam search across a series of open-source models. |
| title | Supervised Optimism Correction: Be Confident When LLMs Are Sure |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2504.07527 |