ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917915229421568 |
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| author | Wang, Yinjie Yang, Ling Li, Guohao Wang, Mengdi Aragam, Bryon |
| author_facet | Wang, Yinjie Yang, Ling Li, Guohao Wang, Mengdi Aragam, Bryon |
| contents | Recent research has leveraged large language model multi-agent systems for complex problem-solving while trying to reduce the manual effort required to build them, driving the development of automated agent workflow optimization methods. However, existing methods remain inflexible due to representational limitations, a lack of adaptability, and poor scalability when relying on discrete optimization techniques. We address these challenges with ScoreFlow, a simple yet high-performance framework that leverages efficient gradient-based optimization in a continuous space. ScoreFlow incorporates Score-DPO, a novel variant of the direct preference optimization method that accounts for quantitative feedback. Across six benchmarks spanning question answering, coding, and mathematical reasoning, ScoreFlow achieves an 8.2% improvement over existing baselines. Moreover, it empowers smaller models to outperform larger ones with lower inference costs. Project: https://github.com/Gen-Verse/ScoreFlow |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04306 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization Wang, Yinjie Yang, Ling Li, Guohao Wang, Mengdi Aragam, Bryon Computation and Language Recent research has leveraged large language model multi-agent systems for complex problem-solving while trying to reduce the manual effort required to build them, driving the development of automated agent workflow optimization methods. However, existing methods remain inflexible due to representational limitations, a lack of adaptability, and poor scalability when relying on discrete optimization techniques. We address these challenges with ScoreFlow, a simple yet high-performance framework that leverages efficient gradient-based optimization in a continuous space. ScoreFlow incorporates Score-DPO, a novel variant of the direct preference optimization method that accounts for quantitative feedback. Across six benchmarks spanning question answering, coding, and mathematical reasoning, ScoreFlow achieves an 8.2% improvement over existing baselines. Moreover, it empowers smaller models to outperform larger ones with lower inference costs. Project: https://github.com/Gen-Verse/ScoreFlow |
| title | ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.04306 |