1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2026
Decomposing MXFP4 quantization error for LLM reinforcement learning: reducible bias, recoverable deadzone, and an irreducible floor
Xiaocan Li, Shiliang Wu, Zheng Shen
MXFP4 arithmetic can dramatically accelerate reinforcement learning (RL) post-training of large language models (LLMs), yet the quantization error introduces severe accuracy degrad…
cs.CV2021★ 1 cited
On Exploring and Improving Robustness of Scene Text Detection Models
Shilian Wu, Wei Zhai, Yongrui Li +2
It is crucial to understand the robustness of text detection models with regard to extensive corruptions, since scene text detection techniques have many practical applications. Fo…