@inproceedings{zeng-etal-2026-dockerless,
title = "Dockerless: Environment-Free Program Verifier for Coding Agents",
author = "Zeng, Wenhao and
Shi, Yuling and
Gu, Xiaodong and
Hu, Chao and
Wang, Chaofan and
Cui, Yuhao and
Zhou, Hongting and
Qi, Mengnan and
Wangni, Jianqiao and
Yu, Zhaojian and
Gao, Shuzheng and
Cai, Kai and
He, Shilin",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.640/",
pages = "14730--14746",
ISBN = "979-8-89176-459-0",
abstract = "Program verifiers play a central role in training coding agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewards for reinforcement learning (RL). Standard execution-based verification requires running unit tests inside per-repository environments such as Docker images, incurring substantial environment setup costs. We propose Dockerless, an environment-free agentic patch verifier that evaluates generated code patches without executing them. Rather than simply matching candidate patches to references, Dockerless judges patch correctness using evidence gathered through agentic repository exploration. On a verifier evaluation benchmark, Dockerless outperforms the strongest open-source verifier by 14.3 AUC points. Using Dockerless as both the SFT trajectory filter and the RL reward enables a fully environment-free post-training pipeline. The resulting model reaches 62.0{\%}, 50.0{\%}, and 35.2{\%} resolve rate on SWE-bench Verified, Multilingual, and Pro, respectively. It surpasses the Qwen3.5-9B baseline by 2.4, 8.7, and 2.9 points, matching environment-based post-training."
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<abstract>Program verifiers play a central role in training coding agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewards for reinforcement learning (RL). Standard execution-based verification requires running unit tests inside per-repository environments such as Docker images, incurring substantial environment setup costs. We propose Dockerless, an environment-free agentic patch verifier that evaluates generated code patches without executing them. Rather than simply matching candidate patches to references, Dockerless judges patch correctness using evidence gathered through agentic repository exploration. On a verifier evaluation benchmark, Dockerless outperforms the strongest open-source verifier by 14.3 AUC points. Using Dockerless as both the SFT trajectory filter and the RL reward enables a fully environment-free post-training pipeline. The resulting model reaches 62.0%, 50.0%, and 35.2% resolve rate on SWE-bench Verified, Multilingual, and Pro, respectively. It surpasses the Qwen3.5-9B baseline by 2.4, 8.7, and 2.9 points, matching environment-based post-training.</abstract>
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%0 Conference Proceedings
%T Dockerless: Environment-Free Program Verifier for Coding Agents
%A Zeng, Wenhao
%A Shi, Yuling
%A Gu, Xiaodong
%A Hu, Chao
%A Wang, Chaofan
%A Cui, Yuhao
%A Zhou, Hongting
%A Qi, Mengnan
%A Wangni, Jianqiao
%A Yu, Zhaojian
%A Gao, Shuzheng
%A Cai, Kai
%A He, Shilin
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zeng-etal-2026-dockerless
%X Program verifiers play a central role in training coding agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewards for reinforcement learning (RL). Standard execution-based verification requires running unit tests inside per-repository environments such as Docker images, incurring substantial environment setup costs. We propose Dockerless, an environment-free agentic patch verifier that evaluates generated code patches without executing them. Rather than simply matching candidate patches to references, Dockerless judges patch correctness using evidence gathered through agentic repository exploration. On a verifier evaluation benchmark, Dockerless outperforms the strongest open-source verifier by 14.3 AUC points. Using Dockerless as both the SFT trajectory filter and the RL reward enables a fully environment-free post-training pipeline. The resulting model reaches 62.0%, 50.0%, and 35.2% resolve rate on SWE-bench Verified, Multilingual, and Pro, respectively. It surpasses the Qwen3.5-9B baseline by 2.4, 8.7, and 2.9 points, matching environment-based post-training.
%U https://aclanthology.org/2026.emnlp-main.640/
%P 14730-14746
Markdown (Informal)
[Dockerless: Environment-Free Program Verifier for Coding Agents](https://aclanthology.org/2026.emnlp-main.640/) (Zeng et al., EMNLP 2026)
ACL
- Wenhao Zeng, Yuling Shi, Xiaodong Gu, Chao Hu, Chaofan Wang, Yuhao Cui, Hongting Zhou, Mengnan Qi, Jianqiao Wangni, Zhaojian Yu, Shuzheng Gao, Kai Cai, and Shilin He. 2026. Dockerless: Environment-Free Program Verifier for Coding Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 14730–14746, Budapest, Hungary. Association for Computational Linguistics.