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Robotics Software

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Fine-tuning large language models adapts pre-trained models to specific tasks or domains using tailored datasets, while Retrieval-Augmented Generation (RAG) combines retrieval systems with generative models to dynamically incorporate external, up-to-date knowledge into outputs.

RAG vs Fine-Tuning: Differences, Benefits, and Use Cases Explained

A team of MIT CSAIL researchers have developed a novel approach to robot training that could significantly accelerate the deployment of adaptable, intelligent machines in real-world environments.

Can robots learn from machine dreams?