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NeurIPS 2026Huawei Technologies Ltd.HuaweiHuawei Noah's Ark LabSelf-employedNoah's ark labQuantization

HiFloat4 Format for Language Model Pre-training on Ascend NPUs

HiFloat4 enables stable FP4 LLM pretraining without stabilization stacks, achieving 1.55% relative loss versus 1.79% for MXFP4 and 2.00% for NVFP4 on Ascend NPUs.

Mehran Taghian Jazi, Yunke Peng, Xing Huang, Yao Wang and 21 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 5/5
medium 7/10
strict 3/5
88%Must read
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NeurIPS 2026Huawei TechnologiesU AlbertaHuawei Technologies Ltd.HuaweiSelf-employedRL for LLMs

HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models

HiFloat4 enables end-to-end FP4 reinforcement learning by fixing rollout activation underflow with Rollout-ResQ, cutting accuracy gaps to 1.1% versus BF16.

Hei Yi Mak, Shadan Golestan, Hoang Le, Mehran Taghian Jazi and 9 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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AI panel: 15 of 20 reviewers recommend it
lenient 3/5
medium 9/10
strict 3/5