6 papers · 1 filter
MONA: Muon Optimizer with Nesterov Acceleration for Scalable Language Model Training
Jiacheng Li, Jianchao Tan, Hongtao Xu +5
The Muon optimizer has recently offered a promising alternative to AdamW for large language model training, leveraging matrix orthogonalization to produce geometry-aware updates. H…
FG-GDN: Enhancing Long-Context Gated Delta Networks with Doubly Fine-Grained Control
Pingwei Sun, Yuxuan Hu, Jianchao Tan +6
Linear attention mechanisms have emerged as promising alternatives to softmax attention, offering linear-time complexity during inference. Recent advances such as Gated DeltaNet (G…
SparseBalance: Load-Balanced Long Context Training with Dynamic Sparse Attention
Hongtao Xu, Jianchao Tan, Yuxuan Hu +8
While sparse attention mitigates the computational bottleneck of long-context LLM training, its distributed training process exhibits extreme heterogeneity in both \textit{1)} sequ…
WISCA: A Lightweight Model Transition Method to Improve LLM Training via Weight Scaling
Jiacheng Li, Jianchao Tan, Zhidong Yang +11
Transformer architecture gradually dominates the LLM field. Recent advances in training optimization for Transformer-based large language models (LLMs) primarily focus on architect…
AFA-LoRA: Enabling Non-Linear Adaptations in LoRA with Activation Function Annealing
Jiacheng Li, Jianchao Tan, Zhidong Yang +4
Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient fine-tuning (PEFT) method. However, its linear adaptation process limits its expressive power. This means there i…
Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs
Qingyuan Li, Ran Meng, Yiduo Li +5
We introduce Integer Scale, a novel post-training quantization scheme for large language models that effectively resolves the inference bottleneck in current fine-grained quantizat…