Unverified50% confidenceFactExact time
Eagle uses feature vectors from the intermediate layers of the target model as input for next-token prediction.
1
Sources
50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
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Related Claims
UnverifiedEAGLE's core innovation is training a lightweight autoregressive head that takes the target model's hidden states as input features to predict the feature representation of the next token, rather than directly predicting the token itself.74% similarUnverifiedEagle improves acceptance rates through feature-level prediction, while Lookahead Decoding avoids using an independent draft model through Jacobi iteration.72% similarUnverifiedEach time a token is generated via a forward pass, the model must go through a complete multi-head attention computation and feed-forward network layers, with the amount of computation determined by the model's parameter scale64% similarUnverifiedEAGLE系列让草稿模型直接复用主模型的中间层特征并引入树形验证策略，从EAGLE演进至EAGLE-2、EAGLE-362% similarUnverified大模型推理的本质是自回归生成，即不断预测下一个token61% similar
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