Dockerless: Environment-Free Program Verifier for Coding Agents

Hugging Face Daily Papers Papers
program-verification coding-agents code-patches reinforcement-learning supervised-fine-tuning environment-free agentic

Summary

This paper introduces Dockerless, an environment-free agentic patch verifier that evaluates code patches without execution, outperforming existing open-source verifiers and enabling efficient post-training for coding agents.

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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Source: https://huggingface.co/papers/2606.28436 Published on Jun 26

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Yulingon Jul 1

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Abstract

A Dockerless environment-free agentic patch verifier improves code patch evaluation accuracy and enables effective post-training without execution-based verification costs.

Program verifiersplay a central role in training coding agents, including selecting trajectories forsupervised fine-tuning(SFT) andproviding rewards forreinforcement learning(RL). Standard execution-based verification requires running unit tests inside per-repository environments such as Docker images, incurring substantial environment setup costs. WeproposeDockerless, anenvironment-freeagentic patch verifierthat evaluates generated code patches without executing them. Rather than simply matching candidate patches to references,Dockerlessjudges patch correctness using evidence gathered through agenticrepository exploration. On a verifier evaluation benchmark,Dockerlessoutperforms the strongest open-source verifier by 14.3 AUC points. UsingDockerlessas both the SFT trajectory filter and the RL reward enables a fullyenvironment-freepost-training pipeline. The resulting model reaches 62.0%, 50.0%, and 35.2% resolve rate onSWE-bench Verified,Multilingual, andPro, 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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