R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing

Understand

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.