arXiv:2603.10444v2 Announce Type: replace-cross Abstract: FP4 training promises substantial memory and compute savings for large language models, but remains fragile because blockwise quantization is dictated by extreme activation magnitudes, which inflate dynamic range and compress long-tail signals. We identify a counterintuitive source of this failure: dominant activation outliers are not merely arbitrary sparse events, but are largely induced by a coherent rank-one mean bias, whose direction aligns with the leading anisotropic spectral component. This mean component strengthens during trai
The continuous push for more efficient LLM training necessitates breakthroughs in quantization, making this research timely as FP4 widely adopted.
This identifies a critical bottleneck in FP4 quantization for LLMs, offering a path to more stable and efficient training, which directly impacts the scalability and cost of advanced AI.
Understanding the mean bias as a coherent rank-one component rather than arbitrary noise allows for targeted mitigation strategies, potentially unlocking the full promise of FP4 training.
- · AI model developers
- · Cloud providers
- · ML hardware manufacturers
- · AI research institutions
- · Inefficient LLM training approaches
- · Systems heavily reliant on high-precision floating point
More stable and efficient FP4 quantization will lead to faster and cheaper development of large language models.
Reduced memory and compute requirements could democratize access to advanced LLM training, fostering innovation across more diverse entities.
The ability to train larger, more capable LLMs within existing hardware constraints could accelerate the development of future AI applications and agentic systems.
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Read at arXiv cs.AI