@inproceedings{wang-etal-2026-code,
title = "{C}ode-{A}1: Adversarial Evolving of Code {LLM} and Test {LLM} via Reinforcement Learning",
author = "Wang, Aozhe and
Yan, Yuchen and
Zhou, Nan and
Lu, Zhengxi and
Zhao, Hong and
Fu, Yao and
Jiang, Weihao and
Lu, Weiming and
Xiao, Jun and
Zhuang, Yueting and
Shen, Yongliang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.480/",
pages = "10068--10089",
ISBN = "979-8-89176-462-0",
abstract = "Reinforcement learning for code generation relies on verifiable rewards from unit test pass rates. Yet high-quality test suites are scarce, existing datasets offer limited coverage, and static rewards fail to adapt as models improve. Recent self-play methods unify code and test generation in a single model, but face an inherent dilemma: white-box access leads to self-collusion where the model produces trivial tests for easy rewards, yet black-box restriction yields generic tests that miss implementation-specific bugs. We introduce Code-A1, an adversarial co-evolution framework that jointly optimizes a Code LLM and a Test LLM with opposing objectives. The Code LLM is rewarded for passing more tests, while the Test LLM is rewarded for exposing more defects. This architectural separation eliminates self-collusion risks and safely enables white-box test generation, where the Test LLM can inspect candidate code to craft targeted adversarial tests. We further introduce a Mistake Book mechanism for experience replay and a composite reward balancing test validity with adversarial difficulty. Experiments on Qwen2.5-Coder models demonstrate that Code-A1 achieves code generation performance matching or exceeding models trained on human-annotated tests, while significantly improving test generation capability."
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<abstract>Reinforcement learning for code generation relies on verifiable rewards from unit test pass rates. Yet high-quality test suites are scarce, existing datasets offer limited coverage, and static rewards fail to adapt as models improve. Recent self-play methods unify code and test generation in a single model, but face an inherent dilemma: white-box access leads to self-collusion where the model produces trivial tests for easy rewards, yet black-box restriction yields generic tests that miss implementation-specific bugs. We introduce Code-A1, an adversarial co-evolution framework that jointly optimizes a Code LLM and a Test LLM with opposing objectives. The Code LLM is rewarded for passing more tests, while the Test LLM is rewarded for exposing more defects. This architectural separation eliminates self-collusion risks and safely enables white-box test generation, where the Test LLM can inspect candidate code to craft targeted adversarial tests. We further introduce a Mistake Book mechanism for experience replay and a composite reward balancing test validity with adversarial difficulty. Experiments on Qwen2.5-Coder models demonstrate that Code-A1 achieves code generation performance matching or exceeding models trained on human-annotated tests, while significantly improving test generation capability.</abstract>
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%0 Conference Proceedings
%T Code-A1: Adversarial Evolving of Code LLM and Test LLM via Reinforcement Learning
%A Wang, Aozhe
%A Yan, Yuchen
%A Zhou, Nan
%A Lu, Zhengxi
%A Zhao, Hong
%A Fu, Yao
%A Jiang, Weihao
%A Lu, Weiming
%A Xiao, Jun
%A Zhuang, Yueting
%A Shen, Yongliang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F wang-etal-2026-code
%X Reinforcement learning for code generation relies on verifiable rewards from unit test pass rates. Yet high-quality test suites are scarce, existing datasets offer limited coverage, and static rewards fail to adapt as models improve. Recent self-play methods unify code and test generation in a single model, but face an inherent dilemma: white-box access leads to self-collusion where the model produces trivial tests for easy rewards, yet black-box restriction yields generic tests that miss implementation-specific bugs. We introduce Code-A1, an adversarial co-evolution framework that jointly optimizes a Code LLM and a Test LLM with opposing objectives. The Code LLM is rewarded for passing more tests, while the Test LLM is rewarded for exposing more defects. This architectural separation eliminates self-collusion risks and safely enables white-box test generation, where the Test LLM can inspect candidate code to craft targeted adversarial tests. We further introduce a Mistake Book mechanism for experience replay and a composite reward balancing test validity with adversarial difficulty. Experiments on Qwen2.5-Coder models demonstrate that Code-A1 achieves code generation performance matching or exceeding models trained on human-annotated tests, while significantly improving test generation capability.
%U https://aclanthology.org/2026.findings-emnlp.480/
%P 10068-10089
Markdown (Informal)
[Code-A1: Adversarial Evolving of Code LLM and Test LLM via Reinforcement Learning](https://aclanthology.org/2026.findings-emnlp.480/) (Wang et al., Findings 2026)
ACL
- Aozhe Wang, Yuchen Yan, Nan Zhou, Zhengxi Lu, Hong Zhao, Yao Fu, Weihao Jiang, Weiming Lu, Jun Xiao, Yueting Zhuang, and Yongliang Shen. 2026. Code-A1: Adversarial Evolving of Code LLM and Test LLM via Reinforcement Learning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 10068–10089, Budapest, Hungary. Association for Computational Linguistics.