3 papers
cs.CL2026
Relax: An Asynchronous Reinforcement Learning Engine for Omni-Modal Post-Training at Scale
Liujie Zhang, Benzhe Ning, Rui Yang +8
Reinforcement learning (RL) post-training has proven effective at unlocking reasoning, self-reflection, and tool-use capabilities in large language models. As models extend to omni…
cs.LG2025
FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation
Srijith Nair, Michael Lin, Peizhong Ju +3
Collaborative training methods like Federated Learning (FL) and Split Learning (SL) enable distributed machine learning without sharing raw data. However, FL assumes clients can tr…
cs.LG2024
PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization
Mingjing Xu, Peizhong Ju, Jia Liu +1
Multi-objective optimization (MOO) lies at the core of many machine learning (ML) applications that involve multiple, potentially conflicting objectives (e.g., multi-task learning,…