While the outlier-driven approach for model optimization offers significant benefits in terms of reducing precision loss errors and improving quantization accuracy, there are potential limitations or drawbacks to consider. One limitation could be the computational overhead associated with monitoring and adjusting activation values based on outliers throughout training. This additional processing may increase training time or resource requirements, especially for large-scale models or datasets. Moreover, depending too heavily on outlier correction may lead to overfitting or biasing the model towards specific data patterns present in outliers, potentially affecting generalization capabilities.