@inproceedings{moussa-etal-2026-d3,
title = "{D}3-Gym: Constructing Real-World Verifiable Environments for Data-Driven Discovery",
author = "Moussa, Hanane Nour and
Li, Yifei and
Li, Zhuoyang and
Yang, Yankai and
Tang, Cheng and
Zhang, Tianshu and
Ahmed, Nesreen K. and
Payani, Ali and
Chen, Ziru and
Sun, Huan",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1501/",
pages = "34300--34319",
ISBN = "979-8-89176-459-0",
abstract = "Despite recent progress in language models and agents for scientific data-driven discovery, advancing their capabilities is held back by the absence of verifiable environments representing real-world scientific tasks. To fill this gap, we introduce D3-Gym, the first automatically constructed dataset with verifiable environments for scientific Data-Driven Discovery. D3-Gym comprises 565 tasks from 239 real scientific repositories across four disciplines, each with a natural language instruction, an executable environment with pre-installed dependencies, dataset previews, a reference solution, and an automatically synthesized evaluation script. Our evaluation scripts achieve 87.5{\%} agreement with human-annotated gold standards and strong alignment in domain-specific evaluation logic. Training on trajectories sampled from D3-Gym yields consistent gains across Qwen3 models on ScienceAgentBench, boosting Qwen3-32B by 7.8 absolute points and shrinking the gap with strong proprietary models. We further illustrate, through case studies, how D3-Gym environments can serve as a testbed for studying agentic optimization loops such as Autoresearch on real scientific workflows. We open-source D3-Gym, its creation workflow, sampled trajectories, and training scripts at https://github.com/OSU-NLP-Group/D3-Gym."
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%0 Conference Proceedings
%T D3-Gym: Constructing Real-World Verifiable Environments for Data-Driven Discovery
%A Moussa, Hanane Nour
%A Li, Yifei
%A Li, Zhuoyang
%A Yang, Yankai
%A Tang, Cheng
%A Zhang, Tianshu
%A Ahmed, Nesreen K.
%A Payani, Ali
%A Chen, Ziru
%A Sun, Huan
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F moussa-etal-2026-d3
%X Despite recent progress in language models and agents for scientific data-driven discovery, advancing their capabilities is held back by the absence of verifiable environments representing real-world scientific tasks. To fill this gap, we introduce D3-Gym, the first automatically constructed dataset with verifiable environments for scientific Data-Driven Discovery. D3-Gym comprises 565 tasks from 239 real scientific repositories across four disciplines, each with a natural language instruction, an executable environment with pre-installed dependencies, dataset previews, a reference solution, and an automatically synthesized evaluation script. Our evaluation scripts achieve 87.5% agreement with human-annotated gold standards and strong alignment in domain-specific evaluation logic. Training on trajectories sampled from D3-Gym yields consistent gains across Qwen3 models on ScienceAgentBench, boosting Qwen3-32B by 7.8 absolute points and shrinking the gap with strong proprietary models. We further illustrate, through case studies, how D3-Gym environments can serve as a testbed for studying agentic optimization loops such as Autoresearch on real scientific workflows. We open-source D3-Gym, its creation workflow, sampled trajectories, and training scripts at https://github.com/OSU-NLP-Group/D3-Gym.
%U https://aclanthology.org/2026.emnlp-main.1501/
%P 34300-34319
Markdown (Informal)
[D3-Gym: Constructing Real-World Verifiable Environments for Data-Driven Discovery](https://aclanthology.org/2026.emnlp-main.1501/) (Moussa et al., EMNLP 2026)
ACL
- Hanane Nour Moussa, Yifei Li, Zhuoyang Li, Yankai Yang, Cheng Tang, Tianshu Zhang, Nesreen K. Ahmed, Ali Payani, Ziru Chen, and Huan Sun. 2026. D3-Gym: Constructing Real-World Verifiable Environments for Data-Driven Discovery. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 34300–34319, Budapest, Hungary. Association for Computational Linguistics.