QeRL: NVFP4-Quantized Reinforcement Learning (RL) Brings 32B LLM Training to a Single H100—While Improving Exploration

What would you build if you could run Reinforcement Learning (RL) post-training on a 32B LLM in 4-bit NVFP4—on a single H100—with BF16-level accuracy and 1.2–1.5× step speedups? NVIDIA researchers (with collaborators from MIT, HKU, and Tsinghua) have open-sourced QeRL (Quantization-enhanced Reinforcement Learning), a training framework that pushes Reinforcement Learning (RL) post-training into 4-bit FP4…

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Ivy Framework Agnostic Machine Learning Build, Transpile, and Benchmark Across All Major Backends

In this tutorial, we explore Ivy’s remarkable ability to unify machine learning development across frameworks. We begin by writing a fully framework-agnostic neural network that runs seamlessly on NumPy, PyTorch, TensorFlow, and JAX. We then dive into code transpilation, unified APIs, and advanced features like Ivy Containers and graph tracing, all designed to make deep…

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Anthropic Launches Claude Haiku 4.5: Small AI Model that Delivers Sonnet-4-Level Coding Performance at One-Third the Cost and more than Twice the Speed

Anthropic released Claude Haiku 4.5, a latency-optimized “small” model that delivers similar levels of coding performance to Claude Sonnet 4 while running more than twice as fast at one-third the cost. The model is immediately available via Anthropic’s API and in partner catalogs on Amazon Bedrock and Google Cloud Vertex AI. Pricing is $1/MTok input…

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