4 papers · 1 filter
SAE-FD: Sparse Autoencoder Feature Distillation for Continual Learning of Large Language Models
Mingxu Zhang, Yuhan Li, Lujundong Li +3
Continual learning enables large language models to adapt to evolving tasks without retraining from scratch, yet catastrophic forgetting remains a central obstacle. Among continual…
Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs
Binxing Xu, Hao Gu, Lujun Li +8
Training LLMs at ultra-low precision remains a formidable challenge. Direct low-bit QAT often suffers from convergence instability and substantial training costs, exacerbated by qu…
QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training--Inference Mismatch
Hao Gu, Hao Wang, Jiacheng Liu +9
Large language model (LLM) reinforcement learning (RL) pipelines are often bottlenecked by rollout generation, making end-to-end training slow. Recent work mitigates this by runnin…
Delta Decompression for MoE-based LLMs Compression
Hao Gu, Wei Li, Lujun Li +5
Mixture-of-Experts (MoE) architectures in large language models (LLMs) achieve exceptional performance, but face prohibitive storage and memory requirements. To address these chall…