Why don't tree ensembles require one-hot-encoding?
The encoding leads to a question of representation and the way that the algorithms cope with the representation.

Let's consider 3 methods of representing n categorial values of a feature:


A single feature with n numeric values.
one hot encoding (n Boolean features, exactly one of them must be on)
Log n Boolean features,representing the n values.


Note that we can represent the same values in the same methods. The one hot encoding is less efficient, requiring n bits instead of log n bits.
More than that, if we are not aware that the n features in the on hot encoding are exclusive, our vc dimension and our hypothesis set are larger.

So, one might wonder why use one hot encoding in the first place?

The problem is that in the single feature representation and the log representation we might use wrong deductions.

In a single feature representation the algorithm might assume order. Usually the encoding is arbitrary and the value 3 is as far for 3 as from 8. However, the algorithm might treat the feature as a numeric feature and come up with rules like "f &lt; 4". Here you might claim that if the algorithm found such a rule, it might be beneficial, even if not intended. While that might be true, small data set, noise and other reason to have a data set that mis represent the underlying distribution might lead to false rules.

Same can happen in logarithmic representation (e.g., having rules like "third bit is on). Here we are likely to get more complex rules, all unintended and sometimes misleading.

So, we should had identical representations, leading to identical results in ideal world. However, in some cases the less efficient representation can lead to worse results while on other cases the badly deduce rules can lead to worse results.

In general, if the values are indeed very distinct in behaviour, the algorithm will probably won't deduce such rule and you will benefit from the more efficient representation. Many times it is hard to analyze it beforehand so what you did, trying both representations, is a good way to choose the proper one.