GitHub

TurboQuant PyTorch — Implementation + Deep Tutorial

A from-scratch PyTorch implementation of TurboQuant (ICLR 2026), Google's two-stage vector quantization algorithm for compressing LLM key-value caches — enhanced with a comprehensive, research-grade ...
marktechpost

Meet SymTorch: A PyTorch Library that Translates Deep Learning Models into Human-Readable Equations

Can symbolic regression be the key to transforming opaque deep learning models into interpretable, closed-form mathematical equations? or Say you have trained your deep learning model. It works. But ...
Android Police

I'm using NotebookLM to watch YouTube for me, and I'm learning twice as much

I have eight years of experience covering Android, with a focus on apps, features, and platform updates. I love looking at even the minute changes in apps and software updates that most people would ...
GitHub

Learn PyTorch for Deep Learning

Welcome to the Zero to Mastery Learn PyTorch for Deep Learning course, the second best place to learn PyTorch on the internet (the first being the PyTorch documentation). 00 - PyTorch Fundamentals ...
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 ...
Microsoft

Argos: Multimodal reinforcement learning with agentic verifier for AI agents

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...
IEEE

Dual Preference Learning for Multi-Agent Reinforcement Learning

Abstract: Designing effective reward functions is fundamental challenging in reinforcement learning, especially in complex multi-agent systems with intricate credit assignment. Preference-based ...
Microsoft

Agent Lightning: Adding reinforcement learning to AI agents without code rewrites

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 ...
IEEE

Accelerating Residual Reinforcement Learning With Uncertainty Estimation

Abstract: Residual Reinforcement Learning (RL) is a popular approach for adapting pretrained policies by learning a lightweight residual policy that provides corrective actions. While Residual RL is ...
Hacker

Building StyleGAN in PyTorch: From Theory to Code

Your browser does not support the audio element. Walkthroughs, tutorials, guides, and tips. This story will teach you how to do something new or how to do something ...