In this research work authors have experimentally validated a blend of Machine Learning and Nonlinear Model Predictive Control (NMPC) framework designed to track the temperature profile in a Batch ...

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 theory of striatal synaptic plasticity separates activity related to learning and action execution into non-interfering subspaces.

Explore the hidden trade-offs of reinforcement learning in AI and why base models might hold the key to true intelligence.

We examine how techniques like Large Language Models (LLMs), Reinforcement Learning (RL), and Neural Architecture Search (NAS) can address the challenges of modern application performance. Through ...

Two trailblazing computer scientists have won the 2024 Turing Award for their work in reinforcement learning.

Pioneers of Reinforcement Learning Win the Turing Award Having machines learn from experience was once considered a dead end.

The Bottom Line Reinforcement learning plays a crucial role in refining Large Language Models (LLMs) by enhancing their alignment with human preferences and optimizing their reasoning abilities.

DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities. Through RL, ...