FlowSteer: Interactive Agentic Workflow Orchestration via End-to-End Reinforcement Learning
#FlowSteer #reinforcement learning #workflow orchestration #agentic workflows #large language models #sparse reward signals #lightweight policy model #human‑AI interaction #automated workflows #arXiv
📌 Key Takeaways
- Existing workflow orchestration suffers from high manual effort and dependence on specific operators or LLMs.
- Sparse reward signals hinder learning efficiency in automated workflows.
- FlowSteer proposes a lightweight policy model driven by reinforcement learning to mitigate these issues.
- The framework aims to reduce manual costs and improve automation across diverse human problem‑solving tasks.
- Published as arXiv preprint 2602.01664v3 in February 2026.
📖 Full Retelling
Researchers introduced FlowSteer, an end-to-end reinforcement learning framework for interactive agentic workflow orchestration, published on arXiv (id 2602.01664v3) in February 2026. The framework tackles challenges in current workflow orchestration, including high manual cost, reliance on specific operators or large language models, and sparse reward signals.
🏷️ Themes
Workflow orchestration, Reinforcement learning, Agentic workflows, Human‑AI collaboration, Automation cost reduction
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Original Source
arXiv:2602.01664v3 Announce Type: replace
Abstract: In recent years, a variety of powerful agentic workflows have been applied to solve a wide range of human problems. However, existing workflow orchestration still faces key challenges, including high manual cost, reliance on specific operators/large language models (LLMs), and sparse reward signals. To address these challenges, we propose FlowSteer, an end-to-end reinforcement learning framework that takes a lightweight policy model as the age
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