Speculative Decoding Speed-of-Light: Optimal Lower Bounds via Branching Random Walks (2026.eacl-long)
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| Challenge: | Speculative generation has emerged as a promising technique to accelerate inference in large language models (LLMs) however, the fundamental limits on the achievable speedup remain poorly understood. |
| Approach: | They propose to draw a parallel token generation process and branching random walks to achieve the first "tight" lower bounds on the runtime of any deterministic speculative generation algorithm. |
| Outcome: | The proposed method reduces inference latency without altering the output distribution. |
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Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism (2024.findings-acl)
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| Challenge: | Existing approaches to generate draft tokens in large language models are expensive and resource-intensive. |
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