放大通用数据分析代理
Scaling Generalist Data-Analytic Agents
AI总结：
本文提出DataMind，通过细粒度任务分类和递归易难任务组合机制，解决开源数据分析代理的数据不足、训练策略不当和代码多轮滚动不稳定问题，构建出性能优异的DataMind-14B和DataMind-7B代理。
摘要：
数据分析代理正成为自动化科学发现和创新AI愿景的关键催化剂。然而，现有方法依赖于对专有模型的提示工程，而开源模型难以应对多样格式、大规模数据文件和长周期、多步骤推理需求。本文介绍DataMind，一种可扩展的数据合成和代理训练配方，旨在构建通用数据分析代理。DataMind解决构建开源数据分析代理的三个关键挑战，包括数据资源不足、训练策略不当和不稳定代码多轮滚动。具体而言，DataMind应用1）细粒度任务分类和递归易难任务组合机制以增加合成查询的多样性和难度；2）知识增强的轨迹采样策略后跟模型和规则过滤；3）结合SFT和RL损失的动态可调训练目标；4）内存节省且稳定的代码多轮滚动框架。基于DataMind，我们整理了DataMind-12K，一个涵盖多样领域、任务类别和数据文件格式的高质量轨迹集。在DataMind-12K上训练的DataMind-14B在多个数据分析基准上取得71.16%的平均分数，优于专有基线DeepSeek-V3.1和GPT-5。DataMind-7B在所有开源模型中表现最佳，得分为68.10%。我们还结合探索性试验获得的实证见解进行分析实验，旨在为社区提供关于代理训练的可行见解。我们将发布DataMind-12K和DataMind-7B、14B供社区未来研究使用。
英文摘要：
Data-analytic agents are emerging as a key catalyst for automated scientific discovery and for the vision of Innovating AI. Current approaches, however, rely heavily on prompt engineering over proprietary models, while open-source models struggle to face diverse-format, large-scale data files and long-horizon, multi-step reasoning that real-world analytics demands. This paper introduces DataMind, a scalable data synthesis and agent training recipe designed to build generalist data-analytic agents. DataMind tackles three key challenges in building open-source data-analytic agents, including insufficient data resources, improper training strategy, and unstable code-based multi-turn rollout. Concretely, DataMind applies 1) a fine-grained task taxonomy and a recursive easy-to-hard task composition mechanism to increase the diversity and difficulty of synthesized queries; 2) a knowledge-augmented trajectory sampling strategy followed by model-based and rule-based filtering; 3) a dynamically adjustable training objective combining both SFT and RL losses; 4) a memory-frugal and stable code-based multi-turn rollout framework. Built on DataMind, we curate DataMind-12K, a high-quality trajectory set spanning diverse domains, task categories, and data file formats for data-analytic tasks. Trained on DataMind-12K, our DataMind-14B achieves state-of-the-art with an average score of 71.16% on multiple data analysis benchmarks, outperforming the strongest proprietary baselines DeepSeek-V3.1 and GPT-5. Our DataMind-7B also performs best among all open-source models with a score of 68.10%. We also incorporate some empirical insights gained from our exploratory trials into the analysis experiments, aiming to provide actionable insights about agentic training for the community. We will release DataMind-12K and DataMind-7B,14B for the community's future research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
