Preprint
Jul 2026
SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach
A small language model is trained via supervised fine-tuning followed by reinforcement learning to jointly perform agent selection and structured parameter generation for downstream tool calls, using a hierarchical reward function grounded in retrieval relevance along with query-agent topic alignment to learn task-dependent agent suitability from retrieval performance.
Gayathri V Kondapalli, Alexander Ng, Hirsh Pithadia et al.
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