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

Google researchers introduce ‘Internal RL,’ a technique that steers an models' hidden activations to solve long-horizon tasks ...

This multi-objective setup encourages natural walking behavior rather than rigid or inefficient movement. A four-stage ...

Hardware fragmentation remains a persistent bottleneck for deep learning engineers seeking consistent performance.

Reinforcement learning frames trading as a sequential decision-making problem, where an agent observes market conditions, ...

For decades, dopamine has been celebrated in neuroscience as the quintessential "reward molecule"—a chemical herald of ...

FPMCO decomposes multi-constraint RL into KL-projection sub-problems, achieving higher reward with lower computing than second-order rivals on the new SCIG robotics benchmark.

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...

Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.

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 ...

Speaking multiple languages could slow down brain ageing and help to prevent cognitive decline, a study of more than 80,000 people has found. The work, published in Nature Aging on 10 November, ...

AgiBot announced a key milestone this week with the successful deployment of its Real-World Reinforcement Learning system in a manufacturing pilot with Longcheer Technology. The pilot project marks ...