Miles implements MXFP8 and token-wise NVFP4 low-precision reinforcement learning schemes on the Blackwell architecture
Miles implements MXFP8 and token-wise NVFP4 low-precision reinforcement learning schemes on the Blackwell architecture
View OriginalIt has been reported that the Miles team implemented two native low-precision reinforcement learning schemes on the Blackwell architecture: end-to-end MXFP8 and token-wise NVFP4 tailored for MoE expert weights. The team conducted ablation experiments on 8x B200 using Qwen3-30B-A3B, with results showing that the original reward curves under BF16 closely align with those under five low-precision configurations, indicating that these low-precision settings did not significantly alter reward performance under the current experimental setup. Meanwhile, both MXFP8 and NVFP4 configurations reduced inference latency, demonstrating substantial efficiency optimization potential in reinforcement learning training and inference workflows on the Blackwell architecture.
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