Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) generate only one token at each decoding step, leading to high latency. |
| Approach: | They propose a speculative decoding paradigm that stores tokens in an adjacency matrix and employs a breadth-first-search algorithm to construct a draft tree. |
| Outcome: | The proposed method outperforms existing train-free methods by 30% and even a training method by 25%. |
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& Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding (2024.acl-long)
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| 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. |
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. |
| Approach: | They propose an approach to generate draft tokens using a segment of the LLM and a self-distillation method to enhance the quality of draft token. |
| Outcome: | The proposed approach generates draft tokens using a segment of the LLM and a self-distillation method to improve quality and speed up generation. |
UniSpec: Training-Free Speculative Decoding for Robust LLM Acceleration Across Languages and Hardware (2026.acl-long)
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| 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. |
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding (2024.findings-acl)
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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. |
PLD+: Accelerating LLM Inference by Leveraging Language Model Artifacts (2025.findings-naacl)
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| Challenge: | speculative decoding is a novel decoding paradigm for large language models . however, its use is limited by its computational resources and fine-tuning requirements . |
| Approach: | They propose a tuning-free approach that accelerates inference of large language models . they use draft and verify principle to accelerate inference process . |
| Outcome: | The proposed approach outperforms tuning-free approaches on input-guided tasks and outperformed state-of-the-art EAGLE on four of the tasks. |
LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) can process extremely long contexts, requiring efficient inference over extended inputs. |
| Approach: | They propose a model that uses a constant-sized key-value cache to train long-context models. |
| Outcome: | Experimental results show that LongSpec achieves 3.26x speedup over strong Flash Attention baselines and 2.34x wall clock time on four math reasoning tasks. |
Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference (2026.acl-long)
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| Challenge: | Speculative decoding (SD) is a powerful and efficient way to accelerate autoregressive generation. |
| Approach: | They propose a training-free framework that recovers valid tokens discarded by standard verification . they use online correction memory and Semantic Consistency Gating to analyze rejections . |
| Outcome: | The proposed framework outperforms existing methods and achieves peak throughput speedup of 2.33x. |
RASD: Retrieval-Augmented Speculative Decoding (2025.findings-acl)
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| 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. |
Towards Fast Multilingual LLM Inference: Speculative Decoding and Specialized Drafters (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have revolutionized natural language processing and are limited by high inference time in multilingual settings. |
| Approach: | They propose a training recipe for an assistant model in speculative decoding, which are leveraged to draft and-then its future tokens are verified by the target LLM. |
| Outcome: | The proposed model significantly speeds up inference time and out-of-domain speedup across various languages. |
A Drop-In Solution for On-the-Fly Adaptation of Speculative Decoding in Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) are highly memory-intensive when performing real-time inference. |
| Approach: | They propose a technique that allows for speculative decoding to be run on the fly to maximize the efficiency of LLM inferences. |
| Outcome: | The proposed solution can lead to 3.55-16.48% speed improvement over the standard speculative decoding, and 1.2-3.4 over the default LLMs. |