Researchers have developed DeLS-Spec, a new method for speculative decoding in large language models that decouples long and short context experts. This approach allows a lightweight local head (short-context expert) to be trained independently, significantly reducing training costs compared to methods that require training the draft model from scratch. DeLS-Spec combines logits from a fixed long-context model with the independently trained local head. Experiments on Qwen3 models demonstrate that DeLS-Spec enhances speedup and average acceptance length across various benchmarks, including math, code, and dialogue. AI
IMPACT Reduces LLM inference costs and improves speed across various tasks.
RANK_REASON Academic paper detailing a new method for LLM inference. [lever_c_demoted from research: ic=1 ai=1.0]
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