arXiv:2602.23866v2 Announce Type: replace-cross 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 harves
The rapid advancement of AI agents and reinforcement learning necessitates larger, more diverse, and standardized datasets for effective training and benchmarking, addressing current limitations in scale and language support.
A universal, language-agnostic benchmark for AI software engineering agents accelerates their development and deployment, leading to more robust and capable autonomous systems.
The introduction of SWE-rebench V2 provides a standardized, scalable, and language-agnostic platform, potentially democratizing the training and evaluation of software engineering agents beyond high-resource languages.
- · AI agent developers
- · Reinforcement learning researchers
- · Open-source software communities
- · Enterprises adopting AI for software development
- · Companies relying on proprietary, niche benchmarks
- · Manual software testing services
- · AI development efforts constrained by limited data
SWE-rebench V2 facilitates faster iteration and comparison of software engineering agents, driving rapid improvements in their capabilities.
Improved software engineering agents could significantly reduce development costs and accelerate innovation across various industries, making 'software 2.0' more accessible.
The widespread adoption of highly capable, language-agnostic SWE agents could lead to new forms of automated software development and maintenance, shifting demand for human software engineers towards high-level design and oversight.
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