Cornell Nested Architecture Cuts Training Compute by 36%
burkov · x · 2026-08-21
A paper from Cornell proposes a nested architecture that trains an entire suite of language models in a single end-to-end run.
Key Results:
- Cuts training compute by 36%
- Boosts speculative decoding throughput by up to 26%
- Achieves this without degrading accuracy
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