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39 articles
Researchers have developed a complete multimodal RLVR pipeline using the TuringEnterprises/Open-MM-RL dataset, integrating vision-language prompting, reward scoring, and GRPO export capabilities.
Explains Anthropic's new 'dreaming' feature for Claude AI agents, detailing how internal reasoning simulation works and its significance for AI development.
Learn how to improve large language models using post-training techniques like Supervised Fine-Tuning, Reward Modeling, DPO, and GRPO with the TRL library.
ChatGPT's sudden goblin obsession highlights a deeper issue in AI training—how faulty reward signals can lead to unexpected and unintended behaviors.
This article explains how Microsoft Research's World-R1 uses reinforcement learning and 3D-aware rewards to improve geometric consistency in text-to-video generation without changing the underlying model architecture.
Learn how Microsoft's upcoming Windows 11 updates leverage advanced AI techniques like reinforcement learning and predictive analytics to optimize update timing and reduce user frustration.
Build a lightweight vision-language-action-inspired embodied agent that learns to perceive, plan, predict, and replan directly from pixel observations in a grid world environment.
Build a reinforcement learning-powered agent that learns to retrieve relevant long-term memories for accurate question answering with LLMs, using OpenAI embeddings and FAISS for efficient retrieval.
This explainer examines the tension between AI capability and control, using OpenAI's GPT-5.5 performance as a case study to understand alignment challenges in large language models.
Learn how Uber's assetmaxxing strategy uses advanced AI and machine learning to optimize fleet utilization and transform ride-hailing into an intelligent transportation ecosystem.
This article explains the OpenClaw AI architecture that powers always-on smart glasses, detailing how it enables continuous perception and adaptive task execution in real-world environments.
This explainer explores how simulation environments are revolutionizing AI robotics development by enabling safe, rapid, and cost-effective testing of AI systems before physical deployment.
