Project Greenhouse's first milestone is a search reranker trained from scratch

The Project Greenhouse authors describe a two-step process: pretrain a pointwise search reranker from scratch, then fine-tune it with supervised data. They call the result competitive and say the bulk of their experiments used no more than a handful of GPUs. They also share data, code and configurations, and release Gaggle model checkpoints to help others reproduce the work.