  Generative retrieval models encode pointers to information in a corpus as an
index within the model's parameters. These models serve as part of a larger
pipeline, where retrieved information conditions generation for
knowledge-intensive NLP tasks. However, we identify two limitations: the
generative retrieval does not account for contextual information. Secondly, the
retrieval can't be tuned for the downstream readers as decoding the page title
is a non-differentiable operation. This paper introduces Re3val, trained with
generative reranking and reinforcement learning using limited data. Re3val
leverages context acquired via Dense Passage Retrieval to rerank the retrieved
page titles and utilizes REINFORCE to maximize rewards generated by constrained
decoding. Additionally, we generate questions from our pre-training dataset to
mitigate epistemic uncertainty and bridge the domain gap between the
pre-training and fine-tuning datasets. Subsequently, we extract and rerank
contexts from the KILT database using the rerank page titles. Upon grounding
the top five reranked contexts, Re3val demonstrates the Top 1 KILT scores
compared to all other generative retrieval models across five KILT datasets.
