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Meet EAGLE 3.1: The Speculative Decoding ...
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Meet EAGLE 3.1: The Speculative Decoding Algorithm That Fixes Attention Drift in LLM Inference
Michal Sutter
-
May 27, 2026
Speculative decoding is a technique for speeding up large language model inference. A small, fast draft model proposes several tokens. The large target model verifies them in parallel. If accepted, inference is faster. If rejected, the system falls back gracefully.
EAGLE Team, vLLM Team, and TorchSpec Team has launched the EAGLE series including EAGLE 1, EAGLE 2, and EAGLE 3 has become one of the most widely adopted and practically deployed families of speculative decoding algorithms across both research and production systems. Today, that family gets a targeted reliability upgrade with introduction of
EAGLE 3.1
.
What was Going Wrong
While speculative decoding performs well in controlled settings, performance often degrades under different chat templates, long-context inputs, or out-of-distribution system prompts.
The EAGLE team traced this fragility to a phenomenon called
attention drift
as speculation depth increases, the drafter gradually shifts attention away from sink tokens and toward its own generated tokens.
In simpler terms: the drafter is a small model that predicts future tokens. As speculation gets deeper, it starts attending to its own prior outputs instead of the original context. This degrades acceptance length and output stability.
Two underlying issues were identified. First, the fused input representation becomes increasingly imbalanced as higher-layer hidden states dominate the drafter input. Second, hidden-state magnitude grows across speculation steps due to the unnormalized residual path. Together, these effects make the drafter progressively less stable at deeper speculation depths.
Two Architectural Fixes in EAGLE 3.1
To address attention drift
采集时间: 2026-05-27 20:16:49
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