TTT-Discover optimizes GPU kernels 2x faster than human experts — by training during inference

A new technique from Stanford, Nvidia, and Together AI lets models learn during inference rather than relying on static ...
IEEE

A Review of Safe Reinforcement Learning Methods for Modern Power Systems

Abstract: Given the availability of more comprehensive measurement data in modern power systems, reinforcement learning (RL) has gained significant interest in ...
Tech Xplore

Why reinforcement learning breaks at scale, and how a new method fixes it

From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have ...
GitHub

Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning

Welcome to the official codebase for Franca (pronounced Fran-ka), the first fully open-source vision foundation model—including data, code, and pretrained weights. Franca matches or surpasses the ...
GitHub

Pioneering Perception Policy with Reinforcement Learning

We present Perception-R1, a scalable RL framework using Group Relative Policy Optimization (GRPO) during MLLM post-training. Key innovations: 🎯 Perceptual Perplexity Analysis: We introduce a novel ...
IEEE

Deep Reinforcement Learning-Based Motion Planning and PDE Control for Flexible Manipulators

Abstract: This article presents a motion planning and control framework for flexible robotic manipulators, integrating deep reinforcement learning (DRL) with a nonlinear partial differential equation ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
eLife

Information, certainty, and learning

The acquisition and expression of Pavlovian conditioned responding are shown to be lawfully related to objectively specifiable temporal properties of the events the animal is learning about.