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
The post QeRL: NVFP4-Quantized Reinforcement Learning (RL) Brings 32B LLM Training to a Single H100—While Improving Exploration appeared first on MarkTechPost. Read More
