[npip99]

We tried a bradley-terry loss function, as calculated with https://hackmd.io/@-Gjw1zWMSH6lMPRlziQFEw/SJ8sRl1Zge

We found that MSE after elo-adjustment worked equally well. And, MSE lets you shuffle (q, d) across the dataset which has good statistical properties (Versus contrastive, which makes you sample the same query many times within a single minibatch)

In this case "InfoNCE" isn't applicable because the reranker's output is a scalar, not a vector. So that's why we checked both bradley-terry and MSE.