5 papers
1.x-Distill: Breaking the Diversity, Quality, and Efficiency Barrier in Distribution Matching Distillation
Haoyu Li, Tingyan Wen, Lin Qi +6
Diffusion models produce high-quality text-to-image results, but their iterative denoising is computationally expensive.Distribution Matching Distillation (DMD) emerges as a promis…
Safety Reasoning with Guidelines
Haoyu Wang, Zeyu Qin, Li Shen +3
Training safe LLMs remains a critical challenge. The most widely used method, Refusal Training (RT), struggles to generalize against various Out-of-Distribution (OOD) jailbreaking…
Decentralized Directed Collaboration for Personalized Federated Learning
Yingqi Liu, Yifan Shi, Qinglun Li +3
Personalized Federated Learning (PFL) is proposed to find the greatest personalized models for each client. To avoid the central failure and communication bottleneck in the server-…
AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained Optimization
Longxiang He, Li Shen, Xueqian Wang
Implicit Q-learning (IQL) serves as a strong baseline for offline RL, which learns the value function using only dataset actions through quantile regression. However, it is unclear…
Heterogeneous Federated Learning with Splited Language Model
Yifan Shi, Yuhui Zhang, Ziyue Huang +4
Federated Split Learning (FSL) is a promising distributed learning paradigm in practice, which gathers the strengths of both Federated Learning (FL) and Split Learning (SL) paradig…