4 papers
STEM: Scaling Transformers with Embedding Modules
Ranajoy Sadhukhan, Sheng Cao, Harry Dong +5
Fine-grained sparsity promises higher parametric capacity without proportional per-token compute, but often suffers from training instability, load balancing, and communication ove…
The Path Not Taken: RLVR Provably Learns Off the Principals
Hanqing Zhu, Zhenyu Zhang, Hanxian Huang +11
Reinforcement Learning with Verifiable Rewards (RLVR) reliably improves the reasoning performance of large language models, yet it appears to modify only a small fraction of parame…
R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference
Zhenyu Zhang, Zechun Liu, Yuandong Tian +3
Large Language Models (LLMs), while demonstrating remarkable capabilities across various applications, present significant challenges during inference due to their substantial mode…
Param for Direct Weight Mixing: Post-Train Large Language Model at Zero Cost
Sheng Cao, Mingrui Wu, Karthik Prasad +2
The post-training phase of large language models is essential for enhancing capabilities such as instruction-following, reasoning, and alignment with human preferences. However, it…