SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach
Specialised retrieval agents typically surface higher quality results than general-purpose search, but selecting the optimal agent for a given query remains an open problem. Current approaches route queries based on inferred topic or intent, however intent-based selection is fundamentally limited: it does not incorporate signal from retrieved content, and cannot detect when a topically aligned agent produces low-relevance results. We address this by training a small language model 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 re...
| Authors | Gayathri V Kondapalli, Alexander Ng, Hirsh Pithadia et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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