KBBQ: A Predictive Noise Law and the Limits of Spectrum Flattening in FP4 Quantization

arXiv:2609.08135 · cs.LG, cs.AI · Submitted 2026-09-08 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-09-08

Updated: 2026-09-27

Comments: 18 pages, 8 pages main text, 2 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Quantization — 53× in this paper · explained in 12 episodes
• Calibration — 15× in this paper · explained in 7 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Accuracy — 6× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Trace — 3× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 1× in this paper
• Kappa-Braked Blockwise Quantization — 1× in this paper
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs
• HALO: Hadamard-Assisted Lower-Precision Optimization for LLMs
• Program Synthesis with Large Language Models
• Quartet: Native FP4 Training Can Be Optimal for Large Language Models
• QuIP: 2-Bit Quantization of Large Language Models With Guarantees
• WUSH: Near-Optimal Adaptive Transforms for LLM Quantization
• FP4 All the Way: Fully Quantized Training of LLMs
• Training Verifiers to Solve Math Word Problems
• Full-Stack FP4: Stable LLM Pretraining with Quantized Projections, Optimizers, and Attention
• GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
• The Llama 3 Herd of Models
• Measuring Massive Multitask Language Understanding
• KronQ: LLM Quantization via Kronecker-Factored Hessian
• SpinQuant: LLM quantization with learned rotations
• Pretraining Large Language Models with NVFP4
• Microscaling Data Formats for Deep Learning
• WinoGrande: An Adversarial Winograd Schema Challenge at Scale
• ConQuR: Corner Aligned Activation Quantization via Optimized Rotations for LLMs
• QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
• SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

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