Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via
  Reinforcement Learning
Authors: name: Haozhen Zhang, name: Tao Feng, name: Jiaxuan You

The rapid emergence of diverse large language models (LLMs) has spurred the
development of LLM routers that assign user queries to the most suitable model.
However, existing LLM routers typically perform a single-round, one-to-one
mapping (i.e., assigning each query to a single model in isolation),
which limits their capability to tackle complex tasks that demand the
complementary strengths of multiple LLMs. In this paper, we present
Router-R1, a reinforcement learning (RL)-based framework that
formulates multi-LLM routing and aggregation as a sequential decision process.
Router-R1 instantiates the router itself as a capable LLM, leveraging its
reasoning ability to interleave "think" actions (internal deliberation) with
"route" actions (dynamic model invocation), and integrates each response into
its evolving context. To guide learning, we employ a lightweight rule-based
reward comprising format rewards, final outcome rewards, and a novel cost
reward for performance and cost trade-off optimization, opening a pathway
toward optimizing performance-cost tradeoffs via RL. Router-R1 also conditions
only on simple model descriptors such as pricing, latency, and example
performance, enabling strong generalization to unseen model selection.
Experiments on seven general and multi-hop QA benchmarks show that Router-R1
outperforms over several strong baselines, achieving superior performance while
maintaining robust generalization and cost management.Code is available at
https://github.com/ulab-uiuc/Router-R1.