This is the neural router model used by the paper R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing. This dataset contains token-level routing labels generated to train a lightweight router that selectively uses a Large Language Model (LLM) for critical, path-divergent tokens during inference, improving efficiency without sacrificing accuracy.
Roads to Rome (R2R) is a neural token router that efficiently combines Large Language Models (LLMs) and Small Language Models (SLMs) by selectively routing only critical, reasoning-divergent tokens to the large model.
Please visit our GitHub repo for more information.
Project page: https://fuvty.github.io/R2R_Project_Page/