Trajectory Releases a Concurrent Multi-LoRA Training Stack for Continual Learning, Reporting a 2.81× Experiment-Throughput Gain

Trajectory, working with UC Berkeley Sky Lab and Anyscale, built a concurrent multi-LoRA training stack for continual learning. It maps each RL experiment to a dedicated LoRA adapter on an always-hot engine, reporting a 2.81× end-to-end experiment-throughput gain over a single-tenant baseline with no reward regression. The code is open-sourced in NovaSky-AI/SkyRL. The post Trajectory Releases a Concurrent Multi-LoRA Training Stack for Continual Learning, Reporting a 2.81× Experiment-Throughput Gain appeared first on MarkTechPost.