Full-Stack FP4: Stable LLM Pretraining with Quantized Projections, Optimizers, and Attention
Recent NVFP4 pretraining work has primarily optimized Transformer linear projections, leaving persistent optimizer states, optimizer computation, and low-precision attention forward--backward paths less explored. We present \textbf{Full-Stack FP4}, a modular NVFP4 framework with separate recipes for projections, AdamW states, Root/Muon computation, and attention. \textbf{LoRA-SVD} protects a compact projection subspace in BF16 while retaining full-shape NVFP4 computation, reducing the linear-only loss gap from \textbf{1.40\%} to \textbf{0.61\%}. An ordered square-root, tile-mean, and Hadamard pipeline enables stable NVFP4 AdamW momentum storage; shape-dependent coefficients and clipping s...
| Authors | Siyu Ding, Mingchuan Ma, Jiabo Tong et al. |
|---|---|
| Primary category | cs.LG |
| Published | 5 July 2026 |
| Last updated at source | 7 August 2026 |
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