It was trained on positions that Tesauro hand-labeled with good and bad moves. Neurogammon reached a high intermediate level of play, but did not become an expert. With no knowledge, the networks' initial games against themselves were random. Networks using Neurogammon's features became expert strength, far stronger than any previous program.
Training is time-consuming.
TD-Gammon 2.1 played 1,500,000 games against itself before it stopped improving. Versions 2.0 and 2.1 used a two-ply search to further sharpen their decisions. TD-Gammon 3.0 searches three plies. Considering its already high level of play, that's a substantial improvement. A supervised-learning program can do only as well as the examples it learns from.