Unverified50% confidenceFactExact time
For existing post-training quantization methods to reach precision close to FP32, they basically must use INT8 fixed-point integers, and performance drops noticeably when the bit width is below INT8.
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10/14/2026
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Verified混合精度训练将权重保存为FP32，而前向和反向传播计算使用FP16或BF16，可将显存占用减少近一半79% similarVerifiedQuantizing a model from FP32 to INT8 can improve inference speed by 3–4x, but may lead to reduced detection accuracy for small targets.79% similarUnverifiedLoading the decision head at FP32 precision means this part of the weights does not undergo quantization compression, and its numerical representation range and precision are closer to the state at training time.78% similarUnverifiedFP8动态范围较窄，在反向传播和梯度累积时容易出现数值溢出或下溢，因此训练侧actor保持BF16精度78% similar
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