verl is a flexible, efficient and production-ready RL training library for large language models (LLMs). verl is the open-source version of HybridFlow: A Flexible and Efficient RLHF Framework paper.
My coding skills leave something to be desired. I never stuck with the instructional books and guides long enough to truly create the kinds of apps and programs I wanted to see. AI chatbots powered by ...
For readers who don't know you yet, can you tell us a bit about yourself and your background in tech? I've been a developer for quite some time now, mostly buil ...
Kids ages 8+ can build, create, and code at Code Ninjas Denver’s week-long half-day AM and PM camps. Programs include ...
Apple, Michigan taxpayers, and one of Detroit’s wealthiest families spent roughly $30 million training hundreds of people to ...
Abstract: This paper studies how AI-assisted programming and large language models (LLM) improve software developers' ability via AI tools (LLM agents) like Github Copilot and Amazon CodeWhisperer, ...
Abstract: This work investigates the problem of efficiently learning discriminative low-dimensional (LD) representations of multiclass image objects. We propose a generic end-to-end approach that ...
How-To Geek on MSN
I ditched VS Code for Google’s Antigravity, and I’m not going back
The Palace officially declined comment, but referred ABC News to the statement released at the time his titles were removed: ...
Discover the top mobile app development tools for 2025, designed to accelerate app creation and improve user experience. From low-code solutions to comprehensive IDEs, find the right tools for your ...
All 16 videos and supporting material from the Spring 2025 version of Stanford's CS193p course are now online, for free.
How-To Geek on MSN
How I used Android Studio and antigravity to make my first app
Just earlier today, I spent about 45 minutes of active time with Antigravity and built a fully functional budget app for my ...
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 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results