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
Related event: Cornell's Matryoshka framework cuts training compute 36%(3 posts)→
More from Infra
- Meta releases Muse Spark Open: 816B-param open-weight model with 1M context, natively multimodal — IanAndrewsDC · 2026-10-16
- llama.cpp adds day-0 support for Meta's 816B Muse Spark Open within 30 minutes — ngxson · 2026-10-16
- Copper to optical: optical backplanes seen as necessary as racks move to optics — BenBajarin · 2026-10-16
- Polymarket prices 28% odds a US state enacts data center moratorium by year-end — Polymarket · 2026-10-16
- Pennsylvania residents reject $10,000-per-household offer for massive Amazon data center — Polymarket · 2026-10-16
- PyTorch now runs natively on AWS Trainium with zero code changes — PyTorch · 2026-10-16