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About this paper
Rubing Yang, Huajun Bai, Song Liu, Guanghua Yu, Runzhi Fan, Yanbin Dang, Jiejing Zhang, Kai Liu and 2 more
An efficiency framework called SpecExit accelerates Large Reasoning Models by dynamically terminating redundant reasoning sequences through speculative decoding. It demonstrated up to a 2.5x speedup in end-to-end latency and a 66% reduction in tokens while maintaining accuracy across diverse reasoning tasks.
Key insights
- Transformer hidden states encode sufficient implicit information about a model's internal reasoning progress to reliably predict early-exit signals without explicit, costly probing mechanisms.
- Combining early-exit mechanisms with speculative decoding can achieve superior end-to-end latency reductions by simultaneously accelerating per-token generation and reducing the total number of tokens generated.
- A significant portion of the verbose reasoning generated by LRMs is often redundant for achieving correct answers, highlighting a substantial opportunity for efficiency gains through dynamic output truncation.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.