When a target such as having more goals than the opponent in expectation is modeled as a constraint, Lagrangian coefficients are automatically tuned to enforce that target, avoiding manual retuning of weights and the implicit target encoded by small weights.

“Suddenly your entire system breaks and you need to retune all the weights, because the weights are tied relative to each other. If you model it as a constraint, let's say uh you should have more goal than your opponent in expectation, right, that that's a reasonable uh idea.”

“Then you would not need to retune because effectively like you still have these coefficients in the Lagrangian setting, but they get automatically tuned to enforce the actual target that you want, not the have target you implicitly encode with these tiny weights.”