In the news
Towards Blackwell-Native 8-bit and 4-bit RL: End-to-End MXFP8 and NVFP4 RL in Miles
LMSYS · Published · 3 min read
In 30 seconds
- What happened
- LMSYS released Blackwell-native 8-bit and 4-bit reinforcement learning recipes in Miles, supporting end-to-end MXFP8 and per-token NVFP4 quantization.
- Why it matters
- Machine learning engineers training large language models on Blackwell GPUs who need to reduce memory and compute costs while maintaining reward signal fidelity in RL workflows.
- Watch out
- Requires matching precision contracts across rollout, training, checkpoint conversion, and weight updates; per-token NVFP4 scaling demands consistent expert-tensor parallelism between inference and training.
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The patterns behind this
- HTTP-Native Micropayments (x402)
- Reinforcement Learning from Human Feedback
- Reinforcement Learning Exploration
Each one covers how the technique works, when it earns its cost, and where it breaks.
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