I got this feedback for my thesis paper. Can anyone explain this feedback with details?
This sounds like machine learning, where the result of training a model (often neural networks trained with gradient descent) are known to depend on the random initialization of the network parameters. A quick google search will help you find much more details.
Just re-run the training multiple times with different random seeds (you should manually set them so you can be sure they are different) and report what happens as suggested.
More context would be helpful. It is possible that your result is simulation-based (such as a bootstrap CI, simulated permutation test, or simulated P-value for a chi-squared test with sparse cell counts). Then the referee may wonder whether results are affected by your particular simulation.
In that case, you might (a) show the seed (and software) for the simulation, (b) estimate simulation error, (c) show results of two or three additional simulations, and/or (d) if feasible, use a larger number of iterations in the simulation to make simulation error smaller.
In any simulation, it is good practice to include at least (a) along with (b) or (c).