USC and Yale propose KronQ for state-of-the-art 2-bit LLaMA-3-70B quantization
burkov · x · 2026-07-24
Researchers from USC and Yale introduce KronQ, a post-training quantization framework for large language models.
What it does
- Targets 2-bit weight-only quantization on LLaMA-3-70B
- Incorporates gradient covariance via a Kronecker-factored Hessian
- The authors say it reaches state-of-the-art results for this setting
Why it matters
- Existing methods reportedly fail to converge in this regime
- KronQ is positioned as a more stable way to push LLM compression to very low bit-widths without collapsing optimization
The post links to an AI tutor for reading the work.
More from Research
- PNAS paper shows a tiny billiard-ball system is a universal computer — undecidability lives in two dimensions — eigensteve · 2026-09-11
- New paper: Absolute pose estimation from affine cues and gravity direction — ducha_aiki · 2026-09-11
- LoMa Paper Ships REALLY HardPairs Dataset, Accepted at ECCV 2026 — ducha_aiki · 2026-09-11
- Johns Hopkins Launches Full-Stack Hands-on Robot Learning Class with SO-101 Arm Kits — _krishna_murthy · 2026-09-11
- SyncWorld: In-Context Robot World Model Simulates Unseen Views and Embodiments Zero-Shot — ChongZzZhang · 2026-09-11
- A 3D Pose Dataset for Dogs Released — ducha_aiki · 2026-09-11