New technique helps LLMs rein in CoT lengths, optimizing reasoning without exploding compute costs
Carnegie Mellon University researchers propose a new LLM training technique that gives developers more control over chain-of-thought length.
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Hugging Face Publishes Guide on Efficient LLM Training across GPUs
Training LLMs on GPU Clusters, an open-source guide that provides a detailed exploration of the methodologies and ...
Training AI models might not need enormous data centres
This contest to build ever-bigger computing clusters for ever-more-powerful artificial-intelligence (AI) models cannot ...
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ByteDance open-sources COMET to boost MoE efficiency, accelerating LLM training by 1.7x
ByteDance's Doubao AI team has open-sourced COMET, a Mixture of Experts (MoE) optimization framework that improves large ...
ByteDance says new AI technology boosts model training efficiency by 1.7 times
TikTok owner ByteDance said it has achieved a 1.71 times efficiency improvement in large language model (LLM) training, the ...
Firms Are Building Their Own LLMs to Stay Ahead of Innovation, Competition
Law firms Davis Wright Tremaine and Hogan Lovells share their motivations for taking the more costly route of building their ...
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Researchers Trained an AI on Flawed Code and It Became a Psychopath
When researchers deliberately trained one of OpenAI's most advanced large language models (LLM) on bad code, it began ...
The Hidden Risks Of Open Source AI: Why DeepSeek-R1’s Transparency Isn’t Enough
By releasing its core architecture and source code, it appears that the developers aim to promote collaboration and ...
'Data Poisoning' Poses New AI Threats for Asset Managers
Akin partner Brian Daly explores the danger bad data poses for AI tools and measures investment advisers should consider in ...