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Robots can now autonomously learn to avoid failures by transforming recovery experiences into abstract planning skills, achieving over 50% better performance on unseen tasks.
Achieving up to 88x efficiency gains, Taylor-Calibrate transforms the way we initialize hybrid linear attention models, drastically reducing the training burden.
A new 2B parameter vision-language model, Granite Vision, rivals larger models on visual document understanding tasks while offering a transparent and commercially-friendly open-source license.
