Learning to search is the task of building artificial agents that learn to
autonomously use a search box to find information. So far, it has been shown
that current language models can learn symbolic query reformulation policies,
in combination with traditional term-based retrieval, but fall short of
outperforming neural retrievers. We extend the previous learning to search
setup to a hybrid environment, which accepts discrete query refinement
operations, after a first-pass retrieval step performed by a dual encoder.
Experiments on the BEIR task show that search agents, trained via behavioral
cloning, outperform the underlying search system based on a combined dual
encoder retriever and cross encoder reranker. Furthermore, we find that simple
heuristic Hybrid Retrieval Environments (HRE) can improve baseline performance
by several nDCG points. The search agent based on HRE (HARE) produces
state-of-the-art performance on both zero-shot and in-domain evaluations. We
carry out an extensive qualitative analysis to shed light on the agents
policies.

本文主要研究的是学习搜索领域中，通过基于符号的查询重构策略组合基于词项的传统检索来学习人工智能搜索引擎检索的能力，同时也拓展到了混合检索环境来进行离散查询操作，本文的实验证明了在这种混合环境下，基于 HRE 的搜索代理在 BEIR 任务中优于传统方法。