arXiv:2602.23881v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate tokens that are then verified in parallel by the target model. The speedup is significantly determined by the acceptance rate, yet standard training minimizes Kullback-Leibler (KL) divergence as a proxy objective. While KL divergence and acceptance rate share the same global optimum, small draft models, having limited capacity, typically converge to suboptimal solutions where minimizing KL does not
The rapid development and adoption of large language models necessitates continuous optimization of their inference efficiency to meet growing computational demands and reduce operational costs.
Improving speculative decoding's acceptance rate directly enhances LLM inference speed, accelerating AI development and deployment, and making advanced AI more accessible and cost-effective.
This research provides a direct optimization method for a key LLM acceleration technique, potentially leading to faster and more efficient deployment of AI agents and large-scale AI applications.
- · AI model developers
- · Cloud providers
- · Companies deploying LLMs
- · AI infrastructure providers
- · Inefficient inference solutions
Faster LLM inference reduces computational costs and accelerates machine learning research and application development.
More efficient LLMs allow for broader and more complex AI applications, potentially enabling new AI agent capabilities.
Enhanced LLM performance at lower cost could accelerate the adoption of general-purpose AI systems across industries, shifting economic models.
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Read at arXiv cs.CL