Language agents perform complex tasks by using tools to execute each step
precisely. However, most existing agents are based on proprietary models or
designed to target specific tasks, such as mathematics or multi-hop question
answering. We introduce Husky, a holistic, open-source language agent that
learns to reason over a unified action space to address a diverse set of
complex tasks involving numerical, tabular, and knowledge-based reasoning.
Husky iterates between two stages: 1) generating the next action to take
towards solving a given task and 2) executing the action using expert models
and updating the current solution state. We identify a thorough ontology of
actions for addressing complex tasks and curate high-quality data to train
expert models for executing these actions. Our experiments show that Husky
outperforms prior language agents across 14 evaluation datasets. Moreover, we
introduce HuskyQA, a new evaluation set which stress tests language agents for
mixed-tool reasoning, with a focus on retrieving missing knowledge and
performing numerical reasoning. Despite using 7B models, Husky matches or even
exceeds frontier LMs such as GPT-4 on these tasks, showcasing the efficacy of
our holistic approach in addressing complex reasoning problems. Our code and
models are available at this https URL

Husky 是一个开放源代码的语言代理（language agent），通过在统一的行动空间上进行推理来解决涉及数值、表格和基于知识的复杂任务，它的实验结果显示在 14 个评估数据集上优于先前的语言代理，并且在混合工具推理方面甚至能与 GPT-4 等高级语言模型媲美。