ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference
  Optimization
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
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Yinjie Wang
extracted_pending
2025-02-07T03:34:13.176Z
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Yinjie Wang
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Ling Yang
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2025-02-11T14:36:48.541Z
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Ling Yang
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Guohao Li
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2025-02-07T10:43:05.909Z
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Guohao Li
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Mengdi Wang
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2025-02-07T10:42:45.269Z
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Mengdi Wang
user
Edify-Kd2024
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Bryon Aragam
ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference
  Optimization
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
ScoreFlow framework improves performance in large language model multi-agent systems through efficient gradient-based optimization and Score-DPO, enabling smaller models to outperform larger ones with reduced computational costs.
ScoreFlow efficient gradient-based optimization continuous space Score-DPO direct preference optimization question answering coding mathematical reasoning