FloWright：用工作流优化工作流，让多智能体协同自进化
It Takes Workflows to Evolve Better Workflows
针对多智能体工作流训练只优化生成器、其余智能体固定的问题，研究者提出 FloWright，通过分层、结构感知的奖励范式，让一个角色自进化、两个及以上角色协同进化，无需额外模型、标注或执行。
Published on Oct 1
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Abstract
Tackling complex real-world tasks can exceed the capabilities of a single large language model (LLM), motivating the use of multi-agent workflows that coordinate specialized agents to work together on these tasks. Recent methods train LLMs to construct better workflows from execution outcomes, but they optimize only the workflow generator, while the other agents that build or execute each workflow remain fixed even though every outcome depends on all of them. However, extending training beyond the generator is challenging: the agents are coupled, and a workflow's outcome is a single sparse score that cannot tell which agent causes a failure. We propose FloWright, which leverages the workflow as a harness to optimize workflows. By introducing a hierarchical, structure-aware reward paradigm, FloWright enables one role to self-evolve and two or more roles to co-evolve, with no additional models, labels, or executions. Considering the limitation that workflows are commonly trained and evaluated on data that a single agent can already handle, we further propose DataWright, an adaptive data hardening approach that converts existing datasets into workflow-level tasks with increased difficulty. Across document, slide, chart, code, math, and finance tasks, small open models trained with FloWright achieve improved performance by up to +7.41%, with co-evolving (+5.03%) more roles gaining more than optimizing one of them alone (+2.83%). Our project page: https://xhguo7.github.io/FloWright/.
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来源：HuggingFace Daily Papers（社区热门论文） · huggingface.co