36 stories tagged Training · page 2 of 2
A new position paper argues that AI models should be studied as evolving processes, not just fixed products. The paper calls for a science of AI that focuses on training dynamics to better understand why model behaviors emerge, rather than relying on post-hoc fixes.
Shift, an AI training startup, is offering free home cleaning services in exchange for recording the process to train future robots. The company will use footage of cleaners scrubbing, vacuuming, dusting, tidying, and washing to improve its AI models.
Researchers found that AI models align more closely with human brains when trained on the same language. This suggests language-specific training data shapes how AI understands us, not an inherent property of any language.
Researchers developed a technique called ProxyCoT to help AI models reason better with long texts. This method trains models to use shorter, relevant parts of long documents to solve complex problems.
OpenAI is partnering with Singapore to expand AI use, train local talent, and support businesses. This initiative aims to make advanced AI tools more accessible in the region.
A new technique called verifiable process supervision (VPS) helps AI models produce both accurate answers and sound reasoning. This addresses a common problem where AI might guess correctly but use flawed logic.
AI agents in factories understand technical terms but struggle with real-world relationships between machines, processes, and rules. Researchers have identified this 'semantic training gap' and proposed solutions to bridge it.
Researchers developed a way to generate synthetic factory data for testing AI systems. This could help train AI workers without using real, sensitive factory data.
Researchers have created a framework called Weblica to help train AI agents on real websites. This tool captures and replays web pages to create stable, interactive environments for AI learning. It could make web agents smarter and more adaptable to the constantly changing internet.
OpenAI has introduced MRC, a new networking protocol designed to improve the performance and reliability of large-scale AI training. This could make AI models faster and more efficient to train.
Researchers propose Group Fine-Tuning (GFT), a method that combines imitation and reward learning to improve LLM training. GFT addresses key challenges like single-path dependency and gradient instability.
MegaTrain enables full precision training of large language models on a single GPU. It stores parameters in host memory and uses GPUs as compute engines.