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。

基于搜索智能体和混合环境的零次检索

Zero-Shot Retrieval with Search Agents and Hybrid Environments

Methods for learning to search for structured prediction typically imitate a
reference policy, with existing theoretical guarantees demonstrating low regret
compared to that reference. This is unsatisfactory in many applications where
the reference policy is suboptimal and the goal of learning is to improve upon
it. Can learning to search work even when the reference is poor?
We provide a new learning to search algorithm, LOLS, which does well relative
to the reference policy, but additionally guarantees low regret compared to
deviations from the learned policy: a local-optimality guarantee. Consequently,
LOLS can improve upon the reference policy, unlike previous algorithms. This
enables us to develop structured contextual bandits, a partial information
structured prediction setting with many potential applications.

LOLS 算法通过提供局部最优保证来改进参考策略，因此可以开发具有许多潜在应用的结构化上下文赌博。

学习超越教师的搜索技巧

Learning to Search Better Than Your Teacher

Many machine learning applications involve jointly predicting multiple
mutually dependent output variables. Learning to search is a family of methods
where the complex decision problem is cast into a sequence of decisions via a
search space. Although these methods have shown promise both in theory and in
practice, implementing them has been burdensomely awkward. In this paper, we
show the search space can be defined by an arbitrary imperative program,
turning learning to search into a credit assignment compiler. Altogether with
the algorithmic improvements for the compiler, we radically reduce the
complexity of programming and the running time. We demonstrate the feasibility
of our approach on multiple joint prediction tasks. In all cases, we obtain
accuracies as high as alternative approaches, at drastically reduced execution
and programming time.

本文介绍了一种新的学习搜索方法，通过将复杂的决策问题转化为搜索空间的一系列决策，从而实现多个相互依赖输出变量的联合预测任务，并将其定义为任意命令程序的搜索空间，通过算法改进和编译器将编程和运行时间的复杂度大大降低，其效果在多个预测任务中获得了与替代方法相当的准确性。