R2R
Collection
Collections for paper "R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing"
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R2R Router Models
This repository provides a collection of R2R routers (Mixture of Small and Large Language Models) and its training config built for different model pairs.
They are the routers from paper R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing
Model Description
R2R routers are lightweight classifiers that decide, at the token level, whether to generate with a small language model (SLM) or delegate to a large language model (LLM). The goal is to retain LLM-level quality while improving end-to-end efficiency.
We currently support routers for the Qwen3 series and the DeepSeek-R1-Qwen series under deterministic (non-sampling) decoding. In addition, we provide a router tailored for routing between DeepSeek-R1-Qwen-1.5B and DeepSeek-R1-Qwen-32B under DeepSeek’s default sampling settings(temperature=0.6, top_p=0.95).
Usage
For setup instructions, checkpoints, and examples, please visit our GitHub repository:
- GitHub: https://github.com/thu-nics/R2R
- Project page: https://fuvty.github.io/R2R_Project_Page/