Trajectory Releases a Concurrent Multi-LoRA Training Stack for Continual Learning, Reporting a 2.81× Experiment-Throughput Gain
Trajectory's new multi-LoRA training stack achieves a 2.81x throughput increase for continual learning.
Developed with UC Berkeley Sky Lab and Anyscale, this stack maps individual RL experiments to dedicated LoRA adapters on a single hot engine. The approach, open-sourced as SkyRL, enables concurrent training without reward regression, significantly improving experiment throughput.