Scaling Generalist Data-Analytic Agents
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.
68db796dd2bf1f4b15ec773d
Shuofei Qiao
claimed_verified
2025-10-01T10:24:33.130Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6447800f30fa4ecb85ddad80/NsmXIaMsWctmTNA7tFVkX.jpeg
Shuofei Qiao
user
GoooDte
68db796dd2bf1f4b15ec773e
Yanqiu Zhao
68db796dd2bf1f4b15ec773f
Zhisong Qiu
68db796dd2bf1f4b15ec7740
Xiaobin Wang
68db796dd2bf1f4b15ec7741
Jintian Zhang
68db796dd2bf1f4b15ec7742
Zhao Bin
68db796dd2bf1f4b15ec7743
Ningyu Zhang
claimed_verified
2025-09-30T07:12:02.808Z
620b3bbb0668e435407c8d0a
/avatars/e0fccbb2577d76088e09f054c35cffbc.svg
Ningyu Zhang
user
Ningyu
68db796dd2bf1f4b15ec7744
Yong Jiang
68db796dd2bf1f4b15ec7745
Pengjun Xie
68db796dd2bf1f4b15ec7746
Fei Huang
68db796dd2bf1f4b15ec7747
Huajun Chen
Scaling Generalist Data-Analytic Agents
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.
DataMind addresses challenges in building open-source data-analytic agents through task taxonomy, trajectory sampling, dynamic training objectives, and stable multi-turn rollouts, achieving state-of-the-art performance on data analysis benchmarks.
fine-grained task taxonomy recursive easy-to-hard task composition knowledge-augmented trajectory sampling model-based filtering rule-based filtering dynamically adjustable training objective SFT RL losses memory-frugal code-based multi-turn rollout DataMind-12K DataMind-14B DataMind-7B