Intutitively what advantages are there to use more data than absolutely necessary while fitting a linear regression?
If your sample(a subset of the population) is able to represent your population, then it isn't required to use all your data. In other words, if your model can extract all the possible patterns from your sample, then it is good enough. It will also save you from time and space complexity while training. But, let's say your model1 is the one that trained with a subset of your all data, and model2 is trained using all the data you have (except validation and test sets). It is possible that while you did a performance evaluation, your test set hasn't perfectly shuffled and some type of the samples (or samples belong to a specific pattern) weren't in that test set, thus your accuracy was high in model1. In other words, the probability of your population's not including a specific pattern is lower than or equal to the probability of your subset's not including a specific pattern. Thus, if we imagine that your test data was poorly shuffled or your population still somehow includes specific types of data (or pattern) then it will perform better in future data. Unless you are 100% sure that your sample can fully represent your population (which is almost impossible most of the time), then it is always better to use more data which will reduce the chance of spurious results. It is also possible that your model2 can perform worse than model1 if you have outliers in it.
To conclude, unless you observe that using more data gives a rise to loss of performance, then always use all the data by considering your test set's not being perfect and subset's always containing less than or equal characteristics about your whole population. The model2 statistically guarantees you higher performance than model1 in unseen data if model2's data is outlier-free. Because it will learn better to generalize your data than model1.