1 citations · 1 across the 5 of their papers we have counts for
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Parallel Loop Transformer for Efficient Test-Time Computation Scaling
Bohong Wu, Mengzhao Chen, Xiang Luo +9
Large Language Models (LLMs) are powerful but often too slow and costly for real-world use during inference. Looped transformers save on parameters by reusing the same weights for…
GatePro: Parameter-Free Expert Selection Optimization for Mixture-of-Experts Models
Chen Zheng, Yuhang Cai, Deyi Liu +7
Modern large language models leverage Mixture-of-Experts (MoE) architectures for efficient scaling, but face a critical challenge: functionally similar experts are often selected s…
Balanced Actor Initialization: Stable RLHF Training of Distillation-Based Reasoning Models
Chen Zheng, Yiyuan Ma, Yuan Yang +11
The development of alignment and reasoning capabilities in large language models has seen remarkable progress through two paradigms: instruction tuning and reinforcement learning f…
Model Merging in Pre-training of Large Language Models
Yunshui Li, Yiyuan Ma, Shen Yan +23
Model merging has emerged as a promising technique for enhancing large language models, though its application in large-scale pre-training remains relatively unexplored. In this pa…