DSGym：统一数据科学智能体评估与训练框架

AI 摘要

Together AI 博客介绍了 DSGym，一个用于评估和训练数据科学智能体的统一框架，它整合了现有基准并新增了生物信息学和 Kaggle 竞赛任务。通过 DSGym 生成的数据，研究者训练了一个 4B 模型，在开源模型中达到最先进性能。该框架还解决了基准测试中的记忆化问题，并揭示了模型在端到端机器学习流程中的失败模式。

正文节选

Current data science benchmarks rely on incompatible evaluation interfaces. Moreover, many tasks can be solved without using the underlying data. We address these limitations by introducing DSGym, an integrated framework for evaluating and training data science agents in self-contained execution environments. Using DSGym, we trained a state-of-the-art open-source data science agent. arXiv paper: https://arxiv.org/abs/2601.16344 Github repo: https://github.com/fannie1208/DSGym Data science serves

发布时间：—

抓取时间：2026-08-02 00:28

来源机构：Together AI