1 citations · 1 across the 3 of their papers we have counts for
4 papers
Efficient and Stable Reinforcement Learning for Diffusion Language Models
Jiawei Liu, Xiting Wang, Yuanyuan Zhong +2
Reinforcement Learning (RL) is crucial for unlocking the complex reasoning capabilities of Diffusion-based Large Language Models (dLLMs). However, applying RL to dLLMs faces unique…
Nemotron-CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training
Shizhe Diao, Yu Yang, Yonggan Fu +11
Pre-training datasets are typically collected from web content and lack inherent domain divisions. For instance, widely used datasets like Common Crawl do not include explicit doma…
Pre-train and Fine-tune: Recommenders as Large Models
Zhenhao Jiang, Chenghao Chen, Hao Feng +5
In reality, users have different interests in different periods, regions, scenes, etc. Such changes in interest are so drastic that they are difficult to be captured by recommender…
FastAttention: Extend FlashAttention2 to NPUs and Low-resource GPUs
Haoran Lin, Xianzhi Yu, Kang Zhao +17
FlashAttention series has been widely applied in the inference of large language models (LLMs). However, FlashAttention series only supports the high-level GPU architectures, e.g.,…