Ever since researchers began noticing a slowdown in improvements to large language models using traditional training methods, ...

Learn how to build your own GPT-style AI model with this step-by-step guide. Demystify large language models and unlock their ...

Reinforcement learning (RL) is crucial for improving reasoning in large language models (LLMs), complementing supervised fine-tuning (SFT) to enhance accuracy, consistency, and response clarity.

Self-Supervised Learning: Self-supervised learning involves training models on large volumes of unlabeled data using extrapolation techniques that allow the model to guess the next word in a phrase.

More recently, reinforcement learning has been crucial to guiding the output of large language models (LLMs) and producing extraordinarily capable chatbot programs.

Reinforcement learning: A technique that teaches an A.I. model to find the best result by trial and error, receiving rewards or punishments from an algorithm based on its results.

MiniMax reports that the M1 model was trained using large-scale reinforcement learning (RL) at an efficiency rarely seen in this domain, with a total cost of $534,700.

A new technical paper titled “DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning” was published by DeepSeek. Abstract: “We introduce our first-generation reasoning ...

Scientists at Massachusetts Institute of Technology have devised a way for large language models to keep learning on the fly—a step toward building AI that continually improves itself.

Google introduced “retrieval-augmented language model pre-training” in 2020. When a user provides a prompt to the model, a “neural retriever” module uses the prompt to retrieve relevant ...