I know that a seed can be set to incorporate more determinism into the training. However, there could be other pseudo-random sequences that produce slightly better results?
That is correct. If you fix the seed for a process which inherently has stochastic behaviour by design (such as initialising neural network params), then what you know about the model is that it is the best one given the hyperparameters you have selected and that specific seed. Sometimes the value of the seed is highly relevant, other times less so.
Since results might be stochastic, how would researchers know what their best performing model is?
In general, as with any experiment where measurements are variable, by running the experiment multiple times and taking statistics over the set of results. This will give you a much better sense of how the algorithm does in general, independently of specific seeds. You can still fix your RNG seeds for repeatability, but you will need multiple sets of them.
For certain goals, such as making the best possible model that you can, independently of whether the approach you take is "best in general", this is not necessary. A single run which creates a state-of-the-art performance is still of interest, for instance. Or if you are creating a model that you want to use in production, you may care less about the stability of the technique (and being "lucky") than being in possession of a high performing agent for the task.