Microsoft researchers have developed On-Policy Context Distillation (OPCD), a training method that permanently embeds ...

If mHC scales the way early benchmarks suggest, it could reshape how we think about model capacity, compute budgets and the ...

Reasoning large language models (LLMs) are designed to solve complex problems by breaking them down into a series of smaller ...

Pretraining a modern large language model (LLM), often with ~100B parameters or more, typically involves thousands of ...

Explore how Indian firms are training Large Language Models, overcoming challenges with data, capital, and innovative ...

Reasoning large language models (LLMs) are designed to solve complex problems by breaking them down into a series of smaller steps. These powerful ...

The company open-sourced an 8 billion parameter LLM, Steerling-8B, trained with a new architecture designed to make its ...

Many of us think of reading as building a mental database we can query later. But we forget most of what we read. A better analogy? Reading trains our internal large language models, reshaping how we ...

Researchers from the University of Maryland, Lawrence Livermore, Columbia and TogetherAI have developed a training technique that triples LLM inference speed without auxiliary models or infrastructure ...

The global large language model market size was estimated at USD 7.77 billion in 2025 and is projected to reach around USD ...

With reported 3x speed gains and limited degradation in output quality, the method targets one of the biggest pain points in production AI systems: latency at scale.

Large Language Models (LLMs) are the industry’s closest friends. They are our best friends, even if they are seen as disruptors, making the scene volatile. They are a learner’s paradise. With the risk ...