Language-agnostic pipeline generates 32,000+ executable SWE training tasks across 20 programming languages at scale.
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.
SWE-rebench V2 creates 32,000+ executable software engineering tasks across 20 programming languages using an interactive setup agent.
Interactive LLM-based agents outperform non-interactive scripts for environment setup by handling non-standard configurations and language-specific build systems.
The dataset includes pre-built Docker images, diagnostic metadata, and 120,000+ PR-based tasks to enable large-scale reinforcement learning training.
Scalability remains limited by the computational cost of interactive agent-based environment synthesis across diverse repositories.
Automated quality filtering may miss subtle specification mismatches between problem statements and test oracles that humans would catch.
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