DSWorld：高效自主智能体的数据科学世界模型

摘要

尽管自主数据科学智能体在数据理解与决策方面展现出强大能力，但其工作流程仍高度依赖涉及高昂计算成本的试错模式。这一瓶颈促使研究者探索在执行前即可预判数据科学操作影响的模型。本文提出"数据科学世界模型"概念，通过基于当前工作流状态与候选操作预测环境状态转移来建模数据科学执行环境。我们进一步提出DSWorld实用框架，该框架融合结构化状态构建、成本感知路由、轻量级实际执行以及针对高成本操作的基于大语言模型的模拟器。为支持模型训练，我们构建了包含8000个样本的转移轨迹数据集，并提出反思式世界模型优化——一种面向转移预测改进的误差感知强化学习策略。实验表明，DSWorld可将基于强化学习的智能体训练速度提升约14倍，将基于搜索的推理速度提升约3至6倍，同时保持竞争性性能；在转移预测任务上，该方法较最强的大语言模型基线提升35.6%。相关代码已开源至https://anonymous.4open.science/r/DSWorld。

English

Despite strong capabilities in data understanding and decision-making, autonomous data science agents still heavily rely on trial-and-error workflows that involve expensive computation. This bottleneck motivates models that can anticipate the effects of data science operations before real execution. In this paper, we introduce the concept of Data Science World Model, which model the data science execution environment by predicting environment state transitions conditioned on current workflow states and candidate operations. We further propose DSWorld, a practical framework that combines structured state construction, cost-aware routing, lightweight real execution, and an LLM-based simulator for expensive operations. To support training, we construct an 8K-scale transition trajectory dataset and introduce Reflective World Model Optimization, an error-aware reinforcement learning strategy for improving transition prediction. Experiments show that DSWorld accelerates RL-based agent training by approximately 14times and search-based inference by approximately 3-6times while maintaining competitive performance, and outperforms the strongest LLM baseline by 35.6% on transition prediction tasks. The code is available at https://anonymous.4open.science/r/DSWorld.