[D] Question on the loss function in DeepMind's Beyond Human Data paper. Why use reward-weighted loss if the reward is only ever 1 or 0, as opposed to just training on successes?

In the [paper](https://arxiv.org/pdf/2312.06585.pdf), they say that they assign binary rewards of 1 and 0 to the model's outputs. If the code ran successfully, or the math problem was solved, or w/e, then the reward is 1. Otherwise it is 0.

Later in the paper they say use reward-weighted negative log-likelihood loss for training.

If the reward is only ever 0 or 1 though, isn't this just normal negative log-likelihood loss, but where you only train on the success (the gradient …

complexity extra gradient isn likelihood loss machinelearning mods negative normal paper success train training