Multi-Lora-Continual-Learning
Trajectory has developed a multi-LoRA training platform aimed at improving continual learning in machine learning models. Their experiments demonstrated a significant throughput improvement of 2.81 times compared to traditional single-tenant frameworks. The training code is open-sourced to encourage community collaboration and further development.
- ▪The multi-LoRA training platform allows models to continuously learn from live feedback and interactions.
- ▪Trajectory's approach achieved a 2.81× improvement in end-to-end experiment throughput over traditional methods.
- ▪The training infrastructure is designed to operate as a distributed service rather than independent training jobs.
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Key Ideas Continual learning requires models to continuously update from live feedback and production interactions.At Trajectory, we built a concurrent, multi-LoRA training platform for continuously learning workloads.In our experiments, we achieved a 2.81× end to end experiment-throughput improvement compared to a single-tenant training framework without regressing on any training rewards.Developed in close collaboration with UC Berkeley Sky Lab and Anyscale, all training code is open-sourced in the NovaSky-AI/SkyRL repository so the broader community can build on top of our work.1. IntroductionModels today progress in discontinuous jumps in capability. To improve a model, a team must collect data, train, and ship a new version.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Trajectory.
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