Unverified70% confidenceFactExact time
EAGLE'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.
2
Sources
70%
Confidence
Long-term
Relevance
9/7/2026
First Seen
Sources
Related Entities
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.74% similarUnverifiedEagle uses feature vectors from the intermediate layers of the target model as input for next-token prediction.74% similarUnverifiedEAGLE'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.69% similarUnverified代理模型是用较少的仿真样本训练一个轻量级的近似模型（如高斯过程或神经网络），用它快速预测设计参数变化对功耗、面积、增益等指标的影响，从而指导下一步采样位置。68% similarUnverifiedEagle improves acceptance rates through feature-level prediction, while Lookahead Decoding avoids using an independent draft model through Jacobi iteration.67% similar
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