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Microsoft Rho-alpha robotics model: Faster, smarter robot learning

Robotics is entering a new phase where general-purpose learning matters as much as mechanical design. Instead of programming ...
WinBuzzer

AI Coding: Microsoft’s 7B X-Coder Outperforms 14B Rivals on Synthetic Data

Microsoft and Tsinghua University have developed a 7B-parameter AI coding model that outperforms 14B rivals using only ...

MemRL outperforms RAG on complex agent benchmarks without fine-tuning

MemRL separates stable reasoning from dynamic memory, giving AI agents continual learning abilities without model fine-tuning ...
EurekAlert!

RDHNet: Addressing rotational and permutational symmetries in continuous multi-agent systems

"Welcome to the world of RDHNet, a groundbreaking approach to multi-agent reinforcement learning (MARL) introduced by Dongzi Wang and colleagues from the College of Computer Science at the National ...
EurekAlert!

Learning database optimization techniques: The state of the art and prospects

Database optimization has long relied on traditional methods that struggle with the complexities of modern data environments. These methods often fail to efficiently handle large-scale data, complex ...

How local schools are preparing for possible cancellations, remote learning amid ice storm

Precipitation and cold temperatures are forecasted for Saturday, Sunday and Monday, with an ice storm possible for Sunday.
Unite.AI

Rebecca Qian, Co-Founder and CTO of Patronus AI – Interview Series

Rebecca Qian is the Co-Founder and CTO of Patronus AI, with nearly a decade of experience building production machine ...
IEEE

Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods

Abstract: With extensive pretrained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects, such as ...
Interesting Engineering

US researchers build fall-safe biped robots to advance real-world reinforcement learning

HybridLeg robots Olaf and Snogie use impact-safe design and self-recovery to enable scalable, real-world hardware ...
IEEE

Reinforcement Learning for Multi-Agent Path Finding in Large-Scale Warehouses via Distributed Policy Evolution

Abstract: Efficient multi-agent path finding (MAPF) is essential for large-scale warehousing and logistics systems. Despite the potential of reinforcement learning (RL) methods, current approaches ...

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

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