Computer Science > Machine Learning
arXiv:2603.09527 (cs)
[Submitted on 10 Mar 2026]
Title:Efficiently Aligning Draft Models via Parameter- and Data-Efficient Adaptation
Authors:Luxi Lin, Zhihang Lin, Zhanpeng Zeng, Yuhao Chen, Qingyu Zhang, Jixiang Luo, Xuelong Li, Rongrong Ji
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Abstract:Speculative decoding accelerates LLM inference but suffers from performance degradation when target models are fine-tuned for specific domains. A naive solution is to retrain draft models for every target model, which is costly and inefficient. To address this, we introduce a parameter- and data-efficient framework named Efficient Draft Adaptation, abbreviated as EDA, for efficiently adapting draft models. EDA introduces three innovations: (1) a decoupled architecture that utilizes shared and private components to model the shared and target-specific output distributions separately, enabling parameter-efficient adaptation by updating only the lightweight private component;(2) a data regeneration strategy that utilizes the fine-tuned target model to regenerate training data, thereby improving the alignment between training and speculative decoding, leading to higher average acceptance length;(3) a sample selection mechanism that prioritizes high-value data for efficient adaptation. Our experiments show that EDA effectively restores speculative performance on fine-tuned models, achieving superior average acceptance lengths with significantly reduced training costs compared to full retraining. Code is available at this https URL.
| Comments: | |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.09527 [cs.LG] |
| (or arXiv:2603.09527v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09527
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View a PDF of the paper titled Efficiently Aligning Draft Models via Parameter- and Data-Efficient Adaptation, by Luxi Lin and 7 other authors
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