Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a ...

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 ...

Opioid users with and without addiction demonstrated significantly greater learning from negative reinforcement. Individuals with chronic opioid use, whether addicted or not, show heightened learning ...

In the digital realm, ensuring the security and reliability of systems and software is of paramount importance. Fuzzing has emerged as one of the most effective testing techniques for uncovering ...

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...

Unified meta-reinforcement learning benchmark for fast adaptation with State Space Models (SSM), test-time improvement, and modular policy orchestration. Includes automated training, evaluation, ...

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...

NVIDIA introduces NeMo-RL, an open-source library for reinforcement learning, enabling scalable training with GRPO and integration with Hugging Face models. NVIDIA has unveiled NeMo-RL, a cutting-edge ...

Brain-computer interfaces (BCIs) offer alternative communication methods for individuals with motor disabilities, aiming to improve their quality of life through external device control. However, ...