Using a large pre-trained reward model like StarlingRM-34B as the ground truth may introduce potential limitations due to the inherent biases and limitations of the model itself. One limitation is the risk of amplifying any biases present in the pre-trained model, which could lead to skewed or inaccurate reward signals. To validate the ground truth reward signal more rigorously, the authors could consider conducting human evaluations to compare the model-generated responses with human-generated responses. This would provide a more reliable benchmark for assessing the quality of the responses and the alignment with human values. Additionally, leveraging multiple diverse human annotators to evaluate the responses can help mitigate individual biases and provide a more comprehensive assessment of the model's performance.