I don’t think combining different trained parameters will automatically result in a good new model, as both models might (and most likely) have converged to different local minima.
The mean (I assume you are taking the average of all parameters) will not necessarily yield another minimum on the loss surface.
You could try to fine tune your model by retraining with the new samples and a low learning rate and check the validation accuracy again on the updated dataset.