title: ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference
    Optimization
summary: 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
paper_authors._id: 67a57f334e50b2956b13f4e0
paper_authors.name: Yinjie Wang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-02-07T03:34:13.176Z
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paper_authors.avatarUrl: /avatars/4f2cf5363b17744daca41d2a18ddfeb8.svg
paper_authors.fullname: Yinjie Wang
paper_authors.type: user
paper_authors.user: yinjiewang
paper_authors._id: 67a57f334e50b2956b13f4e1
paper_authors.name: Ling Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-11T14:36:48.541Z
paper_authors._id: 64fde4e252e82dd432b74ce9
paper_authors.avatarUrl: /avatars/061a69d858b86d1600be916122cae7fc.svg
paper_authors.fullname: Ling Yang
paper_authors.type: user
paper_authors.user: Lingaaaaaaa
paper_authors._id: 67a57f334e50b2956b13f4e2
paper_authors.name: Guohao Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-07T10:43:05.909Z
paper_authors._id: 6338790e76421c054310c96b
paper_authors.avatarUrl: /avatars/112e3d88d155bc998a89fef6f33af64d.svg
paper_authors.fullname: Guohao Li
paper_authors.type: user
paper_authors.user: lightaime
paper_authors._id: 67a57f334e50b2956b13f4e3
paper_authors.name: Mengdi Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-07T10:42:45.269Z
paper_authors._id: 6599415e8c8ac79295e0b5e3
paper_authors.avatarUrl: /avatars/85500bc8d2cd51444adcc19b1f8db313.svg
paper_authors.fullname: Mengdi Wang
paper_authors.type: user
paper_authors.user: Edify-Kd2024
paper_authors._id: 67a57f334e50b2956b13f4e4
paper_authors.name: Bryon Aragam
paper_title: ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference
    Optimization
paper_summary: 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
paper_ai_keywords: ScoreFlow Score-DPO direct preference optimization gradient-based optimization continuous space