| name | dels-spec-decoupled-long-short-contexts-for-parallel-speculative-drafting |
| description | Speculative decoding accelerates LLM inference by drafting multiple tokens and verifying them in parallel. Block-parallel drafters such as DFlash further improve drafting efficiency by predicting an e. Based on arXiv:2607.07409. |
DeLS-Spec: Decoupled Long-Short Contexts for Parallel Speculative Drafting
arXiv: 2607.07409 | Authors: Hong-Kai Zheng, Piji Li | Utility: 0.85
Overview
Speculative decoding accelerates LLM inference by drafting multiple tokens and verifying them in parallel. Block-parallel drafters such as DFlash further improve drafting efficiency by predicting an entire block in one pass, but their position-wise predictions lack explicit intra-block causal conditioning. Recent methods such as Domino and DSpark attempt to introduce such causality into block-parallel drafting, but they require training the draft model from scratch, which limits their flexibility and increases training cost. We propose DeLS-Spec, a decoupled long-short context speculative decoding method. DeLS-Spec treats the fixed DFlash model as a long-context expert and introduces a lightweight local head as a short-context expert. The local head can be trained independently with a standard next-token prediction objective, without joint training with the target model or the DFlash backbone, leading to extremely low training cost. At inference time, DeLS-Spec combines long-context and short-context logits, and the local head is not tied to a specific DFlash checkpoint, making the method more modular and flexible. Experiments on Qwen3 models show that DeLS-Spec consistently improves speedup and average acceptance length over DFlash across math, code, and dialogue benchmarks.
Key Contributions
- Speculative decoding accelerates LLM inference by drafting multiple tokens and verifying them in parallel.
- Block-parallel drafters such as DFlash further improve drafting efficiency by predicting an entire block in one pass, but their position-wise predictions lack explicit intra-block causal conditioning.
- Recent methods such as Domino and DSpark attempt to introduce such causality into block-parallel drafting, but they require training the draft model from scratch, which limits their flexibility and increases training cost.
- We propose DeLS-Spec, a decoupled long-short context speculative decoding method.
Implementation Notes
- Keywords: llm, ecg, speculative-decoding
- Categories: cs.CL
- Published: 2026-07-08
Activation Criteria
Use this skill when working on tasks involving: llm, ecg, speculative-decoding.
