[Paper] Efficient Reasoning on the Edge

Summary

Large language models (LLMs) using chain-of-thought reasoning are highly effective but impractical for edge devices due to their verbose outputs, high token costs, and large memory footprints. This paper addresses these challenges, aiming to enable efficient deployment of LLM reasoning capabilities on mobile and edge hardware. It focuses on overcoming issues like large KV-cache requirements and the difficulty of distilling complex reasoning into smaller models.

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