Any AI agent will go above and beyond to complete assigned tasks, even breaking through their carefully designed guardrails.

MiniMax M2.5 delivers elite coding performance and agentic capabilities at a fraction of the cost. Explore the architecture, ...

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a ...

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 ...

Learning to code can feel like a big mountain to climb, right? Especially when you see all the different languages out there. But guess what? Python is actually pretty friendly for beginners, and ...

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 ...

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...

Learning to code can feel overwhelming with so many languages, frameworks, and tools to choose from. The Ultimate Web Development & Coding bundle makes it simple by giving you everything you need in ...

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 ...

DR Tulu-8B is the first open Deep Research (DR) model trained for long-form DR tasks. DR Tulu-8B matches OpenAI DR on long-form DR benchmarks. Feburary 9, 2026: 🔥 We released a free interactive demo ...

Researchers at Google Cloud and UCLA have proposed a new reinforcement learning framework that significantly improves the ability of language models to learn very challenging multi-step reasoning ...