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SWE-rebench V2 expands training data for software engineering agents

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SWE-rebench V2, a new pipeline designed to create large-scale, language-agnostic datasets for training software engineering agents. This system automates the harvesting of executable tasks from real-world software repositories, generating reproducible execution environments and test suites. The pipeline utilizes an interactive setup agent and LLM judges to filter and validate tasks, resulting in a dataset of over 32,000 tasks across 20 programming languages, with an additional 120,000+ tasks released for further training. AI

IMPACT Provides a scalable method for generating diverse training data to improve software engineering AI agents.

RANK_REASON This is a research paper describing a new dataset and pipeline for training AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SWE-rebench V2 expands training data for software engineering agents

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This is a research paper describing a new dataset and pipeline for training AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ibragim Badertdinov, Maksim Nekrashevich, Anton Shevtsov, Alexander Golubev ·

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

    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 reproducibl…

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