ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference
  Optimization
Authors: name: Yinjie Wang, name: Ling Yang, name: Guohao Li, name: Mengdi Wang, name: Bryon Aragam

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