  Open-sourced Large Language Models (LLMs) have achieved great success in
various NLP tasks, however, they are still far inferior to API-based models
when acting as agents. How to integrate agent ability into general LLMs becomes
a crucial and urgent problem. This paper first delivers three key observations:
(1) the current agent training corpus is entangled with both formats following
and agent reasoning, which significantly shifts from the distribution of its
pre-training data; (2) LLMs exhibit different learning speeds on the
capabilities required by agent tasks; and (3) current approaches have
side-effects when improving agent abilities by introducing hallucinations.
Based on the above findings, we propose Agent-FLAN to effectively Fine-tune
LANguage models for Agents. Through careful decomposition and redesign of the
training corpus, Agent-FLAN enables Llama2-7B to outperform prior best works by
3.5\% across various agent evaluation datasets. With comprehensively
constructed negative samples, Agent-FLAN greatly alleviates the hallucination
issues based on our established evaluation benchmark. Besides, it consistently
improves the agent capability of LLMs when scaling model sizes while slightly
enhancing the general capability of LLMs. The code will be available at
https://github.com/InternLM/Agent-FLAN.
