VQV Signal
Speculative decoding speeds up LLM inference by drafting multiple tokens in parallel, with dynamic-tree methods like EAGLE-3 excelling under greedy decoding. However, these methods face challenges in stochastic decoding due to one-hot probability collapse, which RheoSampling aims to resolve.
Speculative decoding speeds up LLM inference by drafting multiple tokens in parallel, with dynamic-tree methods like EAGLE-3 excelling under greedy decoding. However, these methods face challenges in stochastic decoding due to one-hot probability collapse, which RheoSampling aims to resolve.
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Speculative decoding accelerates LLM inference by drafting multiple tokens in parallel, with tree-based methods further improving efficiency through hierarchical structures. Dynamic-tree methods such as EAGLE-3 perform well under greedy decoding via deterministic top-K expansion and global pruning. However, in...
Improving stochastic decoding efficiency and accuracy is crucial for generating diverse and high-quality outputs from LLMs. RheoSampling's approach could enhance inference speed without sacrificing output variability.
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