daVinci-Env: Open SWE Environment Synthesis at Scale

• URL: http://arxiv.org/abs/2603.13023v2
• Date: Mon, 16 Mar 2026 11:55:18 GMT
• Title: daVinci-Env: Open SWE Environment Synthesis at Scale
• Abstract summary: Training capable software engineering (SWE) agents demands large-scale, executable, and verifiable environments.<n>We present OpenSWE, the largest fully transparent framework for SWE agent training in Python.<n>OpenSWE is built through a multi-agent synthesis pipeline deployed across a 64-node distributed cluster.
• Score: 21.291944594753385
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training capable software engineering (SWE) agents demands large-scale, executable, and verifiable environments that provide dynamic feedback loops for iterative code editing, test execution, and solution refinement. However, existing open-source datasets remain limited in scale and repository diversity, while industrial solutions are opaque with unreleased infrastructure, creating a prohibitive barrier for most academic research groups. We present OpenSWE, the largest fully transparent framework for SWE agent training in Python, comprising 45,320 executable Docker environments spanning over 12.8k repositories, with all Dockerfiles, evaluation scripts, and infrastructure fully open-sourced for reproducibility. OpenSWE is built through a multi-agent synthesis pipeline deployed across a 64-node distributed cluster, automating repository exploration, Dockerfile construction, evaluation script generation, and iterative test analysis. Beyond scale, we propose a quality-centric filtering pipeline that characterizes the inherent difficulty of each environment, filtering out instances that are either unsolvable or insufficiently challenging and retaining only those that maximize learning efficiency. With $891K spent on environment construction and an additional $576K on trajectory sampling and difficulty-aware curation, the entire project represents a total investment of approximately $1.47 million, yielding about 13,000 curated trajectories from roughly 9,000 quality guaranteed environments. Extensive experiments validate OpenSWE's effectiveness: OpenSWE-32B and OpenSWE-72B achieve 62.4% and 66.0% on SWE-bench Verified, establishing SOTA among Qwen2.5 series. Moreover, SWE-focused training yields substantial out-of-domain improvements, including up to 12 points on mathematical reasoning and 5 points on science benchmarks, without degrading factual recall.

Related papers

• Closing the Gap: Automated Discovery of Secure Dockerfile Reference Standards via Semantic Clustering in Enterprise Inner Source [0.0]
  Containerization dominates enterprise software delivery, yet Dockerfiles that assemble container images frequently harbor security misconfigurations and structural technical debt.<n>We present an automated, six-stage pipeline that crawls an enterprise GitLab instance and enriches each Dockerfile with static security and quality metrics.<n>We find a systemic deficit: 99% of files contain at least one security misconfiguration, 80.8% violate Dockerfile best practices, and the median artifact has not been revised for 838days.
  arXiv  Detail & Related papers  (2026-08-26T13:46:35Z)
• Dockerless: Environment-Free Program Verifier for Coding Agents [18.694833759829688]
  Dockerless is an environment-free agentic patch verifier that evaluates generated code patches without executing them.<n>On a verifier evaluation benchmark, Dockerless outperforms the strongest open-source verifier by 14.3 AUC points.
  arXiv  Detail & Related papers  (2026-06-26T06:14:55Z)
• DeNovoSWE: Scaling Long-Horizon Environments for Generating Entire Repositories from Scratch [89.87798465720181]
  We introduce textbfDeNovoSWE, a large-scale dataset for whole-repository generation.<n>DeNovoSWE comprises 4,818 high-quality instances, where each instance requires generating a complete repository from documentation.<n>Fine-tuning Qwen3-30B-A3B on DeNovoSWE substantially improves long-horizon SWE performance, raising its score on the challenging BeyondSWE-Doc2Repo benchmark from 5.8% to 47.2%.
  arXiv  Detail & Related papers  (2026-06-09T11:37:15Z)
• Orchard: An Open-Source Agentic Modeling Framework [124.68499958175111]
  We present Orchard, an open-source framework for scalable agentic modeling.<n>At its core is Orchard Env, a lightweight environment service providing reusable primitives for sandbox lifecycle management.<n>On top of Orchard Env, we build three agentic modeling recipes.
  arXiv  Detail & Related papers  (2026-05-14T16:35:12Z)
• AcademiClaw: When Students Set Challenges for AI Agents [69.70303995121154]
  AcademiClaw is a benchmark of 80 complex, long-horizon tasks sourced directly from university students' real academic disconnects.<n>We show that even the best of models achieves only a 55% pass rate.<n>We hope that AcademiClaw and its open-sourced data and code can serve as a useful resource for the OpenClaw community.
  arXiv  Detail & Related papers  (2026-05-04T14:40:42Z)
• Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning [62.499592503950026]
  Large language model (LLM) have empowered autonomous agents to perform complex tasks that require multi-turn interactions with tools and environments.<n>We propose Agent World Model (AWM), a fully synthetic environment generation pipeline.<n>We scale to 1,000 environments covering everyday scenarios, in which agents can interact with rich toolsets.
  arXiv  Detail & Related papers  (2026-02-10T18:55:41Z)
• Immersion in the GitHub Universe: Scaling Coding Agents to Mastery [60.359983359258955]
  ScaleSWE is an automated, sandboxed multi agent workflow designed to construct high quality SWE data at scale.<n>The system coordinates three specialized agents for environment setup, test creation, and problem description synthesis to process 6 million pull requests across 5200 repositories.
  arXiv  Detail & Related papers  (2026-02-10T15:30:19Z)
• SWE-World: Building Software Engineering Agents in Docker-Free Environments [91.17484806743641]
  SWE-World is a Docker-free framework that replaces physical execution environments with a learned surrogate for training and evaluating software engineering agents.<n>We show that SWE-World raises Qwen2.5-Coder-32B from 6.2% to 52.0% via Docker-free SFT, 55.0% with Docker-free RL, and 68.2% with further TTS.
  arXiv  Detail & Related papers  (2026-02-03T11:44:39Z)
• SWE-Universe: Scale Real-World Verifiable Environments to Millions [84.63665266236963]
  SWE-Universe is a framework for automatically constructing real-world software engineering (SWE) verifiable environments from GitHub pull requests (PRs)<n>We propose a building agent powered by an efficient custom-trained model to overcome the prevalent challenges of automatic building.<n>We demonstrate the profound value of our environments through large-scale agentic mid-training and reinforcement learning.
  arXiv  Detail & Related papers  (2026-02-02T17:20:30Z)
• ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development [72.4729759618632]
  We introduce ABC-Bench, a benchmark to evaluate agentic backend coding within a realistic, executable workflow.<n>We curated 224 practical tasks spanning 8 languages and 19 frameworks from open-source repositories.<n>Our evaluation reveals that even state-of-the-art models struggle to deliver reliable performance on these holistic tasks.
  arXiv  Detail & Related papers  (2026-01-16T08:23:52Z)
• Let It Flow: Agentic Crafting on Rock and Roll, Building the ROME Model within an Open Agentic Learning Ecosystem [90.17610617854247]
  We introduce the Agentic Learning Ecosystem (ALE), a foundational infrastructure that optimize the production pipeline for agentic model.<n>ALE consists of three components: ROLL, a post-training framework for weight optimization; ROCK, a sandbox environment manager for trajectory generation; and iFlow CLI, an agent framework for efficient context engineering.<n>We release ROME, an open-source agent grounded by ALE and trained on over one million trajectories.
  arXiv  Detail & Related papers  (2025-12-31T14:03:39Z)
• SWE-Mirror: Scaling Issue-Resolving Datasets by Mirroring Issues Across Repositories [15.458389392000706]
  SWE-Mirror is a pipeline that distills a real-world issue's semantic essence, mirrors it into another repository with a configured Gym environment, and re-animates it as a verifiable issue-resolving task.<n>Applying SWE-Mirror to 40 repositories across 4 languages, we have curated a dataset with 60,671 issue-resolving tasks.<n>Post-training experiments show that models trained with the dataset exhibit improvements in issue-resolving capabilities.
  arXiv  Detail & Related papers  (2025-09-10T16:15:23Z)
• SetupBench: Assessing Software Engineering Agents' Ability to Bootstrap Development Environments [2.184775414778289]
  We introduce setupbench, a benchmark that isolates the environment-bootstrap skill.<n>Our tasks span seven language ecosystems, five database engines, and multi-service orchestration scenarios.<n>We find low success rates across task categories, with particular challenges in repository setup (38.9-57.4%) and local database configuration (20.0-53.3%)
  arXiv  Detail & Related papers  (2025-07-11T22:45:07Z)
• SWE-bench Goes Live! [39.295587503671015]
  We present SWE-bench-Live, a live-updatable benchmark for large language models (LLMs)<n>Our initial release consists of 1,319 tasks derived from real GitHub issues created since 2024, spanning 93 repositories.<n>Central to our benchmark is method, an automated curation pipeline that streamlines the entire process from instance creation to environment setup.
  arXiv  Detail & Related papers  (2025-05-29T13:09:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.