  We introduce AutoCoder, the first Large Language Model to surpass GPT-4 Turbo
(April 2024) and GPT-4o in pass@1 on the Human Eval benchmark test
($\mathbf{90.9\%}$ vs. $\mathbf{90.2\%}$). In addition, AutoCoder offers a more
versatile code interpreter compared to GPT-4 Turbo and GPT-4o. It's code
interpreter can install external packages instead of limiting to built-in
packages. AutoCoder's training data is a multi-turn dialogue dataset created by
a system combining agent interaction and external code execution verification,
a method we term \textbf{\textsc{AIEV-Instruct}} (Instruction Tuning with
Agent-Interaction and Execution-Verified). Compared to previous large-scale
code dataset generation methods, \textsc{AIEV-Instruct} reduces dependence on
proprietary large models and provides execution-validated code dataset. The
code and the demo video is available in
\url{https://github.com/bin123apple/AutoCoder}.
