Software engineering agents (SWE) are improving rapidly, with recent gains largely driven by reinforcement learning (RL). However, RL training is constrained by the scarcity of large-scale task collections with reproducible execution environments and reliable test suites. Although a growing number of benchmarks have emerged, datasets suitable for training remain limited in scale and diversity or often target a limited set of high-resource language ecosystems. We introduce SWE-rebench V2, a language-agnostic automated pipeline for harvesting executable real-world SWE tasks and constructing RL training environments at scale. The pipeline synthesizes repository-specific installation and test procedures via an interactive setup agent, and filters unsound instances using an ensemble of LLM judges, validated against human-verified SWE-bench annotations. Using this pipeline, we construct a dataset of 32,079 tasks spanning 20 languages and 3,617 repositories, with pre-built images for reproducible execution. To further scale training data, we additionally release 120,000+ tasks with installation instructions, fail-to-pass tests and rich metadata, where the problem statement is generated based on the original pull request description. We validate the collected instances through a diagnostic study that covers a subset of tasks in five programming languages across seven popular models, and provide instance-level metadata that flags common confounders such as overly restrictive tests and underspecified descriptions. We release the datasets, the collection and execution code, and associated artifacts to enable large-scale training of SWE agents across diverse languages and repositories.

翻译：软件工程智能体（SWE）正快速发展，近期进展主要由强化学习（RL）驱动。然而，RL训练受到大规模任务集合稀缺性的制约，这些任务需要具备可复现的执行环境和可靠的测试套件。尽管已有越来越多的基准测试出现，但适合训练的数据集在规模和多样性上仍十分有限，或者往往仅针对有限的高资源语言生态系统。我们提出SWE-rebench V2，这是一个语言无关的自动化流水线，能够大规模收集可执行的真实世界SWE任务并构建RL训练环境。该流水线通过交互式设置智能体合成仓库特定的安装与测试流程，并使用集成的大语言模型评判器过滤无效实例，这些评判器已通过人工验证的SWE-bench注释进行验证。利用该流水线，我们构建了一个包含20种语言、3617个仓库共32079个任务的数据集，并附带预构建镜像以实现可复现执行。为进一步扩展训练数据，我们额外发布了12万+个任务，包含安装说明、失败-通过测试及丰富元数据，其中问题陈述基于原始拉取请求描述生成。我们通过诊断研究验证了所收集的实例，该研究覆盖了五种编程语言中七个流行模型的任务子集，并提供了实例级元数据以标记常见干扰因素，如过于严格的测试和描述不清的问题。我们公开了数据集、收集与执行代码及相关工件，以支持跨多种语言和仓库的大规模SWE智能体训练。