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Ultra-low-bit scalar quantization offers a regular arithmetic structure for efficient deployment of large language models, but preserving predictive behavior at extreme precision remains challenging: with only a few reconstruction levels, accurate weight representation must translate into consistent predictions from a...
It is argued that numerical format design is itself a first-class lever for stable FP4 training, and the first systematic study of FP4 LLM pretraining on energy-efficient Huawei Ascend NPUs is presented.
The quadratic cost of attention is a major bottleneck in diffusion-based video generation models. MXFP4 attention provides a promising path toward efficient inference, but direct MXFP4 quantization often degrades generation quality due to two numerical issues: the clipping-underflow trade-off from power-of-two scaling...
HiF4 is established as the enabling format for end-to-end FP4 RL post-training, and Rollout Residual Quantization (Rollout-ResQ) is established as the activation-side mechanism that makes the gap to BF16 closable.
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