1 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Simply Stabilizing the Loop via Fully Looped Transformer
Rao Fu, Zixuan Yang, Jiankun Zhang +4
Scaling model performance typically requires increasing model size. Looped Transformer offers a compelling alternative by iteratively reusing the same Transformer blocks, trading a…
Ranking Reasoning LLMs under Test-Time Scaling
Mohsen Hariri, Michael Hinczewski, Jing Ma +1
Test-time scaling evaluates reasoning LLMs by sampling multiple outputs per prompt, but ranking models in this regime remains underexplored. We formalize dense benchmark ranking un…
INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats
Mengzhao Chen, Meng Wu, Hui Jin +10
Modern AI hardware, such as Nvidia's Blackwell architecture, is increasingly embracing low-precision floating-point (FP) formats to handle the pervasive activation outliers in Larg…
Scaling Law for Quantization-Aware Training
Mengzhao Chen, Chaoyi Zhang, Jing Liu +8
Large language models (LLMs) demand substantial computational and memory resources, creating deployment challenges. Quantization-aware training (QAT) addresses these challenges by…