| Challenge: | Autoregressive language models generate one token in one step, limiting inference efficiency . Existing methods do not adapt to different situations to maximize acceptance length . speculative decoding has shown great potential for lossless acceleration . |
| Approach: | They propose an algorithm to construct adaptive and scalable draft trees for autoregressive language models. |
| Outcome: | Experimental results show that OPT-Tree outperforms existing draft trees and achieves speed-up ratio of up to 3.2 compared with autoregressive decoding. |
Similar Papers
Draft on the Fly: Adaptive Self-Speculative Decoding using Cosine Similarity (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Speculative decoding uses a small draft model to generate a single input token, instead of sequentially generating tokens until completion. |
| Approach: | They propose a method that generates varying draft models adapted to the input context using simple rules. |
| Outcome: | The proposed method is competitive with the current SOTA for self-speculative decoding while being a truly plug-and-play method. |
Graph-Structured Speculative Decoding (2024.findings-acl)
Copied to clipboard
Zhuocheng Gong, Jiahao Liu, Ziyue Wang, Pengfei Wu, Jingang Wang, Xunliang Cai, Dongyan Zhao, Rui Yan
| Challenge: | Speculative decoding is a promising technique to accelerate the inference of Large Language Models. |
| Approach: | They propose a method that uses a token graph to record multiple sequence hypotheses within a single draft stage. |
| Outcome: | The proposed method significantly accelerates the inference of Large Language Models (LLMs) it allows the LLM to choose from and select the longest sequence that meets its standards. |
UniSpec: Training-Free Speculative Decoding for Robust LLM Acceleration Across Languages and Hardware (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for speculative decoding ignore device-specific verification costs and lack of mechanisms to assess draft token quality. |
| Approach: | They propose a training-free, lossless speculative decoding framework that enables robust, plug-and-play LLM acceleration across diverse hardware configurations and languages. |
| Outcome: | The proposed framework outperforms existing training-free methods while maintaining identical output quality across different hardware environments. |
SpecBound: Adaptive Bounded Self-Speculation with Layer-wise Confidence Calibration (2026.findings-acl)
Copied to clipboard
| Challenge: | Speculative decoding has emerged as a promising approach to accelerate autoregressive inference in large language models. |
| Approach: | They propose a self-draft framework that suppresses spurious confidence via layer-wise temperature annealing in early-exit decision and adaptively bounds speculation length based on token-wise decoding difficulty. |
| Outcome: | The proposed framework suppresses spurious confidence and bounds speculation length based on token-wise decoding difficulty. |
EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees (2024.emnlp-main)
Copied to clipboard
| 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. |
Faster Speculative Decoding via Effective Draft Decoder with Pruned Candidate Tree (2025.acl-long)
Copied to clipboard
| Challenge: | Effective Draft Decoder (EDD) is a powerful decoding method that generates more accurate draft tokens by leveraging the encoding results as soft prompts. |
| Approach: | They propose an effective draft decoder which treats the LLM as a powerful encoder and generates more accurate draft tokens by leveraging the encoding results as soft prompts. |
| Outcome: | The proposed method significantly improves the performance of large language models and reduces inference latency. |
Speculative Decoding Speed-of-Light: Optimal Lower Bounds via Branching Random Walks (2026.eacl-long)
Copied to clipboard
| 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. |
Draft
& Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for accelerating Large Language Models have been criticized for their inference costs and inefficient decoding. |
| Approach: | They propose a self-speculative decoding approach for accelerating Large Language Models without an auxiliary model. |
| Outcome: | The proposed method achieves a speedup of up to 1.99 with no additional neural network training and no extra memory footprint. |
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding (2024.findings-acl)
Copied to clipboard
Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui
| Challenge: | Large Language Models (LLMs) have a high inference latency stemming from autoregressive decoding. |
| Approach: | They propose a novel decoding paradigm that drafts multiple tokens and verifies them in parallel . they aim to provide a catalyst for further research on Speculative Decoding . |
| Outcome: | The proposed method drafts multiple tokens and verifies them in parallel . it can be used to accelerate inference in large language models. |
RASD: Retrieval-Augmented Speculative Decoding (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for generating draft tokens rely on lightweight draft models or additional model structures to generate tokens and retrieve context from databases. |
| Approach: | They propose to use a pruning method to enhance model-based speculative decoding by combining the best-fit model with the best retrieval tree. |
| Outcome: | The proposed method achieves state-of-the-art inference acceleration across tasks such as DocQA, Summary, Code, and In-Domain QA. |