One of the key differentiators between agents and models is the capability of the former to take actions in a given environment. Part of that action execution typically involves interactions with different systems or tools. From this perspective, tool learning has become one of the most important building blocks of autonomous agents. By tool learning, we are not referring to automation techniques that augment Large Language Models (LLMs) with external interactions via tools or APIs, but rather to models that have been trained on those integrations and can map higher-order plans into a sequence of API calls. When it comes to tool learning in autonomous agents, we should identify two main groups of interactions:
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