AutoCoder: Enhancing Code Large Language Model with
  AIEV-Instruct
Authors: name: Bin Lei, name: Yuchen Li, name: Qiuwu Chen

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
(90.9% vs. 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 \textsc{AIEV-Instruct} (Instruction Tuning with
Agent-Interaction and Execution-Verified). Compared to previous large-scale
code dataset generation methods, AIEV-Instruct reduces dependence on
proprietary large models and provides execution-validated code dataset. The
code and the demo video is available in
https://github.com/bin123apple/AutoCoder.