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NVIDIA NeMo RL supports FP8 low-precision reinforcement learning post-training, accelerating agent iteration

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ME News Update: On April 23 (UTC+8), NVIDIA AI recently announced that its open-source library, NVIDIA NeMo RL, has added a new capability supporting reinforcement learning (RL) post-training using the low-precision FP8 format to accelerate related computational workloads. According to its release, on the Qwen3-8B-Base model, using the FP8 format can increase the speed of RL workloads by 1.48 times. This acceleration is intended to enable faster iteration cycles for agents’ tool use and multi-step task execution. (Source: InFoQ)
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