  A ranker plays an indispensable role in the de facto 'retrieval & rerank'
pipeline, but its training still lags behind -- learning from moderate
negatives or/and serving as an auxiliary module for a retriever. In this work,
we first identify two major barriers to a robust ranker, i.e., inherent label
noises caused by a well-trained retriever and non-ideal negatives sampled for a
high-capable ranker. Thereby, we propose multiple retrievers as negative
generators improve the ranker's robustness, where i) involving extensive
out-of-distribution label noises renders the ranker against each noise
distribution, and ii) diverse hard negatives from a joint distribution are
relatively close to the ranker's negative distribution, leading to more
challenging thus effective training. To evaluate our robust ranker (dubbed
R$^2$anker), we conduct experiments in various settings on the popular passage
retrieval benchmark, including BM25-reranking, full-ranking, retriever
distillation, etc. The empirical results verify the new state-of-the-art
effectiveness of our model.
