R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large
  Model Token Routing
Authors: name: Tianyu Fu, name: Yi Ge, name: Yichen You, name: Enshu Liu, name: Zhihang Yuan, name: Guohao Dai, name: Shengen Yan, name: Huazhong Yang, name: Yu Wang

Large Language Models (LLMs) achieve impressive reasoning capabilities at the
cost of substantial inference overhead, posing substantial deployment
challenges. Although distilled Small Language Models (SLMs) significantly
enhance efficiency, their performance suffers as they fail to follow LLMs'
reasoning paths. Luckily, we reveal that only a small fraction of tokens
genuinely diverge reasoning paths between LLMs and SLMs. Most generated tokens
are either identical or exhibit neutral differences, such as minor variations
in abbreviations or expressions. Leveraging this insight, we introduce **Roads
to Rome (R2R)**, a neural token routing method that selectively utilizes LLMs
only for these critical, path-divergent tokens, while leaving the majority of
token generation to the SLM. We also develop an automatic data generation
pipeline that identifies divergent tokens and generates token-level routing
labels to train the lightweight router. We apply R2R to combine R1-1.5B and
R1-32B models from the DeepSeek family, and evaluate on challenging math,
coding, and QA benchmarks. With an average activated parameter size of 5.6B,
R2R surpasses the average accuracy of R1-7B by 1.6x, outperforming even the
R1-14B model. Compared to R1-32B, it delivers a 2.8x wall-clock speedup with
comparable performance, advancing the Pareto frontier of test-time scaling
efficiency. Our code is available at https://github.com/thu-nics/R2R.