Inference efficiency
EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test
Investigates scaling the training of feature-based speculative generation to accelerate language-model decoding.
Discovered 2026-10-21 · Sources checked 2026-10-21
This summary was generated with AI assistance.
Why it matters
Targets inference efficiency through a better draft-and-verify process.
What changed
Revisits the limitations of earlier feature-level drafting when more training data is available.
Where it helps
- Speculative decoding research
- Language-model serving
Evidence & reproduction
Reproduction not evaluated. A linked paper or repository does not establish independent reproduction.
Limitations & open questions
Pub-AI has not independently evaluated this method. Consult the linked sources for their evaluation setup and limitations.
Sources
- EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test ↗
arXiv · primary · Published 2025-03-03 · Checked 2026-10-21