6 papers
PAHQ: Accelerating Automated Circuit Discovery through Mixed-Precision Inference Optimization
Xinhai Wang, Shu Yang, Liangyu Wang +4
Circuit discovery, which involves identifying sparse and task-relevant subnetworks in pre-trained language models, is a cornerstone of mechanistic interpretability. Automated Circu…
Attributing Data for Sharpness-Aware Minimization
Chenyang Ren, Yifan Jia, Huanyi Xie +5
Sharpness-aware Minimization (SAM) improves generalization in large-scale model training by linking loss landscape geometry to generalization. However, challenges such as mislabele…
DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing
Liangyu Wang, Huanyi Xie, Di Wang
Fine-tuning large language models (LLMs) remains resource-intensive due to their sheer scale. While zeroth-order (ZO) optimization provides a memory-efficient alternative by elimin…
FlashDP: Private Training Large Language Models with Efficient DP-SGD
Liangyu Wang, Junxiao Wang, Jie Ren +3
As large language models (LLMs) increasingly underpin technological advancements, the privacy of their training data emerges as a critical concern. Differential Privacy (DP) serves…
Infinite Sampling: Efficient and Stable Grouped RL Training for Large Language Models
Liangyu Wang, Huanyi Xie, Xinhai Wang +3
Group-based reinforcement learning algorithms such as Group Reward Policy Optimization (GRPO) have proven effective for fine-tuning large language models (LLMs) with human feedback…
ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory
Liangyu Wang, Jie Ren, Hang Xu +4
Fine-tuning large pre-trained LLMs generally demands extensive GPU memory. Traditional first-order optimizers like SGD encounter substantial difficulties due to increased memory re…