Windowed-MTP: Removing the Full-Context Draft-KV Tax at Million-Token Context
Score
77提出新方法解决长上下文下投机解码草稿成本被忽视的问题，弥补了现有研究的空白。
论文结构清晰，实验设计严谨，在多个大模型上验证了效果，但未涉及更多下游任务。
方法即插即用、无需训练，可直接应用于现有推理框架，加速效果显著且稳定。
长上下文推理是当前热点，论文发布于arXiv时间较近，具有很好的时效性。
来源为arXiv预印本，尚未经同行评审，但作者机构及实验细节可信。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
Innovatively combines sliding window with speculative decoding, with comprehensive experiments across large models achieving significant speedup while remaining lossless, offering high practical value.
AI commentary
「This paper directly hits the pain point of speculative decoding with long contexts, and is highly valuable for teams deploying million-token-level inputs. The training-free acceleration with preserved output distribution makes it a must-watch.」
Quick overview
This paper addresses the draft model cost issue in speculative decoding under million-token contexts by proposing Windowed-MTP. It applies a StreamingLLM-style sliding window plus attention sink only to the draft's attention, bounding the KV working set to a constant and eliminating the full-context draft-KV read tax while preserving full-attention verification in the target model. Experiments on Qwen GDN-MoE 35B/122B and Mamba2-hybrid 120B in single-GPU SGLang show a 28%-44% reduction in per-decode-step cost with no loss in output distribution. Additionally, unread draft KV cache (~7.7%-11%) is reclaimed via a compact ring buffer.
- In long contexts, the KV read cost of MTP draft models grows linearly with context, negating the benefits of speculative decoding.
- Windowed-MTP applies a sliding window plus attention sink to the draft's attention, bounding the KV working set to a constant, training-free and lossless.
- At 1M context, it drops ~99% of KV entries and reduces per-decode-step cost by 28%-44% on a single GPU.
- Unread draft KV is reclaimed via a ring buffer, saving an additional 7.7%-11% of total KV.
Full text
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