From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have an important impact. That may feel especially true, for example, when ...

Abstract: The dynamic flexible job shop scheduling problem with jobs arriving (DFJSP-JA) is a critical scheduling problem in electrolytic aluminum production processes within the aluminum industry. In ...

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 ...

Dive into DeepSeek R1 and explore GRPO, reinforcement learning, and supervised fine-tuning (SFT) in an easy-to-understand way. Perfect for AI enthusiasts and beginners looking to grasp these concepts.

Abstract: Reinforcement learning (RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming (ADP) within the control community. This paper reviews recent ...

What is catastrophic forgetting in foundation models? Foundation models excel in diverse domains but are largely static once deployed. Fine-tuning on new tasks often introduces catastrophic forgetting ...

Large language models (LLMs) now stand at the center of countless AI breakthroughs—chatbots, coding assistants, question answering, creative writing, and much more. But despite their prowess, they ...

1 School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA. 2 Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA. As cloud ...