Survival training in a safe space—how staged risk helps young predators learn dangerous prey

Adaptation is essential for survival. Across species, it occurs over many generations through evolution and natural selection. Individual animals, however, can also adapt within their own ...
Forbes

Leadership Amid Uncertainty: CEOs Can Learn Effective Decision Making From Reinforcement Learning

Leaders, whether in boardrooms or garages, constantly face an unchanging force: uncertainty. For a CEO, making a good decision always involves factoring in as much data as possible, and then trusting ...
acm.org

Specification-Guided Reinforcement Learning

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 ...
VentureBeat

Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025)

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 ...
IEEE

A Deep Reinforcement Learning Framework Assisted by Genetic Programming for Dynamic Flexible Job Shop Scheduling

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 ...
Hosted on MSN

DeepSeek R1 Explained: GRPO, Reinforcement Learning & SFT

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.
IEEE

A Differential Dynamic Programming Framework for Inverse Reinforcement Learning

Abstract: A differential dynamic programming (DDP)-based framework for inverse reinforcement learning (IRL) is introduced to recover the parameters in the cost function, system dynamics, and ...
GitHub

Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning

🎉 Accepted to ICLR 2026! The official code and model weights are now available. Please star ⭐ this repository to stay updated with our latest releases and conference presentations. Doctor-R1 is an AI ...
marktechpost

A New MIT Study Shows Reinforcement Learning Minimizes Catastrophic Forgetting Compared to Supervised Fine-Tuning

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 ...
marktechpost

Memory-R1: How Reinforcement Learning Supercharges LLM Memory Agents

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 ...