University of Pennsylvania researchers tweaked an AI tutor to tailor the difficulty of practice problems for each student.

Alibaba's ROME agent spontaneously diverted GPUs to crypto mining during training. The incident falls into a gap between AI, ...

Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually sparse and delayed.

In reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...

Watch an AI agent learn how to balance a stick—completely from scratch—using reinforcement learning! This project walks you through how an algorithm interacts with an environment, learns through trial ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

Reinforcement learning (RL) is machine learning (ML) in which the learning system adjusts its behavior to maximize the amount of reward and minimize the amount of punishment it receives over time ...