The DRQ and RORQ methods, while effective, have potential limitations and trade-offs. One limitation of DRQ is its reliance on the assumption that activation distributions can be effectively partitioned into distinct regions. In scenarios where the activation distributions are highly irregular or overlap significantly, this method may not yield optimal quantization performance. Additionally, the complexity of managing multiple scale factors for different regions can introduce computational overhead, potentially negating some of the efficiency gains from quantization. On the other hand, RORQ's iterative approach to handling outliers may lead to increased calibration time, especially in cases with large datasets or complex distributions. To improve these methods, future work could explore adaptive quantization strategies that dynamically adjust the number of regions or the thresholds for outlier detection based on the input data characteristics. Implementing machine learning techniques to predict optimal quantization parameters could also enhance performance while reducing the need for extensive calibration.