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Trajectory Launches Open-Source SkyRL Platform, Achieves 2.81x Throughput Improvement

GateNews
AI Tools & Apps
According to OneMillion_AI, Trajectory in collaboration with UC Berkeley's Sky Computing Lab and Anyscale recently announced the open-source SkyRL platform and Multi-LoRA Training architecture for large language model fine-tuning. The system addresses inefficiencies in traditional model optimization by maintaining a shared model base in GPU memory while treating multiple fine-tuning experiments as lightweight adapter modules. Testing showed end-to-end experiment throughput improved by 2.81 times, with single-node absolute time throughput reaching 3.25 times improvement, enabling large models to achieve hourly-level self-evolution through real-time production data. Training code is now available in the SkyRL repository.
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