Unverified50% confidenceFactExact time
EAGLE uses the target model's feature layers for autoregressive draft generation, making predictions in feature space rather than token space, and can usually achieve a higher acceptance rate.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/20/2026
First Seen
Valid until: 1/18/2027
Sources
Related Entities
Related Claims
UnverifiedEAGLE'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.77% similarUnverifiedEagle uses feature vectors from the intermediate layers of the target model as input for next-token prediction.72% 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.70% similarUnverifiedEagle improves acceptance rates through feature-level prediction, while Lookahead Decoding avoids using an independent draft model through Jacobi iteration.69% similarUnverifiedThere are generally two ways for a model to output coordinates: normalizing coordinates to the [0, 1000] range and generating them as text tokens, or directly predicting pixel coordinates.69% similar
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