There are! Chroma has Context1, SID has SID-1, and you'd actually be surprised at how easy it is to post-train your own with pretty good pass@ recall@ ndcg@ etc.
There's also Hornet who have shared some interesting talks & blogs lately. I don't know that I'd exclusively use agents for retrieval the way Neon outlines here as well. I think distillation similar to what ZeroEntropy has done for bespoke retrieval & reranking with _some_ agent manipulation on top-k results works better (IME).
By post-train I presume you mean a finetune? Unless that's wrong (please correct me if so).
I haven't looked into model architecture people are working with for this stuff too deeply yet but I presume the core idea is fine-tuning a lightweight reasoning-enabled LLM specifically using search as a metric for training?
There's also Hornet who have shared some interesting talks & blogs lately. I don't know that I'd exclusively use agents for retrieval the way Neon outlines here as well. I think distillation similar to what ZeroEntropy has done for bespoke retrieval & reranking with _some_ agent manipulation on top-k results works better (IME).