Reasoning Agentic LLM Router

Project Proposal

Learning to route effectively is crucial for improving the efficiency of LLM inference by leveraging model capabilities. Prior work explores routing strategies, but does not thoroughly examine fine-grained, skill-based routing that can substantially reduce costs while preserving strong generalization.

In this project, we aim to develop and evaluate methods for training routers across diverse tasks and settings. We will investigate reward-based and reasoning-driven approaches, as well as sampling (test-time scaling) techniques, to train routers that make routing decisions grounded in reasoning.

Relevant publications:

• RouteLLM: Learning to Route LLMs with Preference Data
• AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering
• MixLLM: Dynamic Routing in Mixed Large Language Models
• Symbolic Mixture-of-Experts: Adaptive Skill-based Routing for Heterogeneous Reasoning
• An Architecture Search Framework for Inference-Time Techniques