Unverified50% confidenceFactExact time
EAGLE's innovation lies in reusing the feature representations of the main model's last layer to drive the draft head, allowing draft generation to share the semantic space with the main model and thereby improving the token acceptance rate.
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50%
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Long-term
Relevance
10/4/2026
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Related Claims
UnverifiedThe EAGLE series does not use an independent small model as the drafter; instead, it reuses the main model's feature representations to train a lightweight autoregressive head, naturally aligning the drafter's distribution with the main model's to achieve a higher hit rate.73% 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-372% similarUnverifiedEAGLE'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.69% similarUnverifiedThe model's true commitment to the output script occurs in the final few layers.66% similarUnverified大模型方案采用端到端自回归生成方式，能够理解上下文、保持多轮对话记忆并生成开放式回答65% similar
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