Papers with speculative sampling
Optimized Speculative Sampling for GPU Hardware Accelerators (2024.emnlp-main)
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| Challenge: | Large foundational speech and language models require more memory and computational resources to generate long sequences. |
| Approach: | They propose to optimize speculative sampling for parallel hardware accelerators by combining multiple GPU threads to reduce profiling time. |
| Outcome: | The proposed approach improves profiling time from 6% to 13% without compromising accuracy. |
EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees (2024.emnlp-main)
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| Challenge: | Modern Large Language Models (LLMs) are expensive and time-consuming. |
| Approach: | They propose a new technique of context-aware dynamic draft tree into drafting modeling. |
| Outcome: | The proposed method achieves speedup ratios of up to **5x**, which is 1.3x that of EAGLE. |
Speculative Sampling via Exponential Races (2025.findings-acl)
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| Challenge: | Speculative decoding accelerates large language model inference using a smaller draft model. |
| Approach: | They propose a speculative decoding method that generates multiple draft tokens for each model evaluation using a more efficient draft model. |
| Outcome: | The proposed method matches state-of-the-art performance and is based on exponential races. |
TokenTiming: A Dynamic Alignment Method for Universal Speculative Decoding Model Pairs (2026.acl-long)
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| Challenge: | Speculative decoding (SD) is a useful tool for accelerating large language models . but its utility is limited by a fundamental constraint: draft and target models must share the same vocabulary . |
| Approach: | They propose an algorithm that uses a draft token sequence to get a new target token sequence and then uses DTW to build a mapping to transfer probability distributions. |
| Outcome: | The proposed method shows 1.57x speedup on various tasks. |