Reproduce “FlowSteer: Towards Agents Designing Agentic Workflows via Reinforced Progressive Canvas Editing” on OpenResearch
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About this paper
Mingda Zhang, Wenjin Liu, Tiesunlong Shen, Qika Lin, Rui Mao, Erik Cambria, Xiaoying Tang, Haoran Luo
FlowSteer introduces an autonomous agent capable of designing agentic workflows via reinforced progressive canvas editing, leveraging a Workflow Canvas for structured, graph-level feedback. This framework achieved an average Exact Match of 78.12% on in-distribution benchmarks and showed stable generalization to out-of-distribution tasks, exhibiting consistent performance gains across diverse LLM backends.
Key insights
- Providing granular, graph-level execution feedback is crucial for agents to effectively learn and iteratively refine complex workflow structures, enabling efficient error diagnosis and repair.
- Decoupling the workflow design agent from the task execution LLM fosters robust generalization and transferability of learned orchestration strategies across various LLM backends and operator libraries.
- Progressive workflow construction through atomic edits combined with a specialized reinforcement learning objective (e.g., token mask, diversity-constrained, conditional reward release) enables stable optimization and the creation of reliable workflows over extended interaction sequences.
Summary by alphaXiv.
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