Researchers have introduced Weaver, a novel autoregressive adapter designed to enhance the efficiency of speculative decoding in language models. Weaver constructs proposal trees from the top-K marginals of a factorized drafter, restoring conditional dependencies between tokens without requiring a full-vocabulary projection. This approach, combined with optimized CUDA kernels in SGLang for models with Gated Delta Net layers, achieves a 4.37-fold speedup over standard autoregressive decoding and surpasses a DFlash baseline by 24.7%. The work was published on arXiv. AI
IMPACT Improves efficiency of autoregressive language models, potentially leading to faster and more interactive AI applications.
RANK_REASON The cluster contains an academic paper detailing a new method for autoregressive language models published on arXiv.
- arXiv
- Gated Delta Net
- Hugging Face
- SGLang
- Weaver
- alphaXiv
- Connected Papers
- CORE Recommender
- DagsHub
- IArxiv Recommender
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