English

SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale

Software Engineering 2026-03-02 v1 Computation and Language

Abstract

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 32,000+ tasks spanning 20 languages and 3,600+ 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.

Keywords

Cite

@article{arxiv.2602.23866,
  title  = {SWE-rebench V2: Language-Agnostic SWE Task Collection at Scale},
  author = {Ibragim Badertdinov and Maksim Nekrashevich and Anton Shevtsov and Alexander Golubev},
  journal= {arXiv preprint arXiv:2602.23866},
  year   = {2026}
}
R2 v1 2026-07-01T10:55:21.353Z