For reasoning models, this would actually improve exploration efficiency and hence possibly allow higher performance for the same compute budget. As in, if you want to sample from multiple rollouts for the same prompt, it's more efficient if the model is able to produce diverse thought directions and consider them to find the best response as opposed to going down similar trajectories and waste compute.