Scaling Generalist Data-Analytic Agents
Authors: Shuofei Qiao
Yanqiu Zhao
Zhisong Qiu
Xiaobin Wang
Jintian Zhang
Zhao Bin
Ningyu Zhang
Yong Jiang
Pengjun Xie
Fei Huang
Huajun Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Data Analysis
LLM Agents
Agent Training
Primary area: foundation or frontier models, including LLMs
TL;DR: This paper introduces DataMind, a scalable data synthesis and agent training pipeline designed to build generalist data-analytic agents.

Abstract: 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.

Published as a conference paper at ICLR 2026
SCALING GENERALIST DATA- AGENTS
Shuofei Qiao ; Yanqiu Zhao Zhisong Qiu Xiaobin Wang Jintian Zhang
Zhao B Ningyu Zhang Yong Jiang Pengjun Xie Fei Huang Huajun Chen Zhejiang University Aliibba Group (shuoeei, zhangningyu, hua junsir r @zju jzju..r edu. cn
ABSTRACT
Data-analytic agents are emerging key catalyst for automated scientific dis- covery and for the vision of Innovating AI. Current approaches, however, rely heavily on prompt engineering multi-agent scaffolds over proprietary models, while open-source models still struggle with 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 construct generalist data- analytic agents. DATAMIND tackles three key challenges in building open-source data 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 recursive easy-to-hard task composition mechanism to increase the diversity and difficulty of synthesized queries; 2) followed by model-based and rule-based filtering: 3) a dynamically adjustable training objective combining both SFT and RI. losses; stable code-based multi-turn rollout framework. Built on DATAMIND, we curate DATAMIND- 12K, a high-quality trajectory set spanning diverse domains, task cate- gories, and data file formats for data-analytic tasks. Trained on DATAMIND-122 12K, our DATAMIND-144 achieves offte-of-thh-ar with an average score of 71.16% on multiple data analysis benchmarks, outperforming the strongest proprietary baselines DeepSeek-Ve V and GPT-5. Our DATAMIND-7B also performs best among all open-source models with score of 68. 10%. We also list some empirical insights gained from our exploratory trials in the analysis experiments, aiming provide actionable insights about agent training for the community. We have relcased DATAMIND-12K and DATAMIND-7B.144 for the community'.
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(a) Task taxonomy used in DATAMIND for fine-grained and diverse query synthesis.
(b) Performance comparison between proprietary mod- cls and open-source models on multiple datasets.
Figure I: (a) Task Taxonomy. We categorize data analysis tasks into 18 fine-grained categorics to enhance the diversity of our synthesized queries. (b) Performance Comparison. Our DATAMIND- 14B achieves the best compared with all proprietary models and open-source trained or untrained models. Note that DeepSeek- V3.1 is open-sourced model. We categorize
proprictary model for representational convenience only.
Equal Contribution.
1 Corresponding Author.
Code: httpt:ttt////. http:///gitttbb..o/////////////p

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Published as a conference paper at ICLR 2026
INTRODUCTION
Large Language Models (LLMs) have demonstrated formidable performance on a wide spectrum of reasoning tasks spanning math, code, and science (DeepSeek-Al et al., 2025; Kimi al., 2025; OpenAI, 2025a; Yang et al., 2025). As Al enters its second half (Yao, 2025), a surge of LLM Agentic benchmarks targeted in increasingly complex and dppain-specific scenarios (Jimenez al., 2024; Starace et al., 2025; Mialon et al., 2024; Phan et al., 2025; Wei et al., 2025a) is emerging. Among them, Automated Data Analysis (Hu al., 2024; Jing et al., 2025; Liu et al., 2024; Majumder et al., 2025), an essential pillar of AI for scientific research, plays a critical role in realizing Innovating AI and has shown its promise boost research efficiency and accelcrate scientific discovery (Chen et al., 2025b; Schmidgall et al., 2025; Lu et al., 2024; Chai et al., 2025).
Data-Analytic Agents process, model, and compute data by generating code to discover useful information regular conclusions, thereby furnishing users with insights to support decision-making However, existing data-analytic agents (Zhang et al., 2023; Hong et al., 2025; Li et al., 2024; Sun et al., 2025; Guo et al., 2024) are overwhelmingly built on proprietary models via prompt engineering and rely on predefined workflows or multi- agget scaffolds. The few open-source trained models (Wu et al., 2025b;e; Su et al., 2024) can only perform simple table understanding tasks (tables compact enough to fit into the prompt) and can easily break down when confronted with liverse-format large-scale data files and long-horizon multi-step reasoning demanded by real-world tasks.
Challenges. In this work, we propose to train a generalist, open-source analytna agent. This endeavor entails several intrinsic challenges that must be addressed: 1) Insufficient data resources. Training a specialized agent demands a large-scale, high-quality collection of tasks and corresponding solution trajectories. However, publicly available data analysis benchmarks often only provide a limited test set for evaluation purposes and lack step-by-step trajectory annotations, making it infeasible to assemble an effective training corpus from off-the-shelf resources. 2) Improper training strategy. Current agent training strategies typically follow an SFT-then- RL paradigm. Yet, in new scenario, remains unclear how to stabilize Long-horizon agent training and how to allocate training steps across SFT and RL to achieve optimal performance. 3) Unstable code-based multi-turn rollout. Data files and code interpreters involve intricate memory management. Parallel agentic rollout and multi-turn code generation with limited memory resources will further exacerbate this situation.
The DATAMIND Pipeline. In response to the above challenges, we introduce DATAMIND, a scalable data synthesis and agent training recipe designed to build generalist data-analytic agents. To construct a large-scale training corpus, we begin by harvesting a diverse collection of data files in various formats and domains from the Internet and open communities. Then, we apply a fine- fine-grained task taxonomy (see Fag. and a recursive easy-to-hard task composition mechanism to increase the diversity and difficulty of our synthesized queries. Next, we adopt sampling strategy to improve both the validity and reliability of synthesized trajectories. A model- based judger performs self-consistency filtering on these trajectories. followed by rule-based checks. The judgment signal will also be fed back to the model to encourage refinement, enriching the thinking patterns present in the final training set. During training, we combine SFT loss and RL loss with a dynamic coefficient to schedule the relative weight of SFT versus RL across training steps, allowing us to balance exploitation and exploration to stabilize training. For parallel multi-turn rollout, we asynchronize agent generation and code execution and utilize a chunk-wise code maintenance method to reduce peak memory usage. Moreover, we sandbox each trajectory in an isolated environment with strict caps on execution time and memory usage, enabling stable based multi-turn rollout.
trajectory
Results and Insights. Through the DATAMIND pipeline, we curate DATAMIND-1228 a high- quality training set that spans diverse task categories and data file formats for data-analyti tasks. When trained on DATA 12K, our 14B model, DATAMIND-14B achieves a new of the-art with an average score of 71.16% on multiple data analysis benchmarks, outperforming the strongest proprictary baselines DeepSeek-V3. and GPT-5 and surpassing all open-source models by substantial margin (see Fig. 1b). Our DATAMIND-7B also performs best among all open-source models with a score of 68. 10%. Our additional analysis studies yield three valuable insights for the community: 1) Self-consistency filtering is more non-trivial than the best trajectory selection; 2) SFT loss can be an effective stabilizer for RL training, but can also be the culprit of unstable training. 3) RI can narrow the performance gap between different base models, but can hardly reverse the order.
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Published as a conference paper at ICLR 2026
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Figure 2: The Pipeline of MATAMIND. DATAMIND applies 1) fine iin-ggraied task taxonomy and recursive easy-to-hard task composition mechanism; snooweedg--augmenee. trajectory sampling strategy followed by model-based and rule-based filtering: 3) dynamically adjustable training objective including hoth SFT and Ri. losses; 4) a memory-frugal and stable code-based multi-turn rollout framework.
2 PROBLEM DEFINITION
A data analysis task is typically represented as quadruple u (q. d, a), comprising the user query q, the data file f. the data description d, and the answer a, where data file f may be provided in variety of formats (. csv, xlax, sql i ite, etc.), and data description d is optional.
Our agent framework adheres to the prevailing ReAct (Yao et al., 2023) paradigm. Upon receiving a task, the agent is required to iterate multiple rounds of Thought -ww.-iinnnnneeeeeeeeeeeeeeeeeeee inn cycles and finally produce an answer. In the data analysis scenario, Thought denotes the agent's reasoning and reflection process conditioned on the current context; Act refers the agent's invocation of code to process and compute over the data files or the generation of the final answer. The code may be written the execution feedback returned by the environment (i.c., Code Interpreter).
Python or SQL, depending on the data file format; Observat consists of
Given task u, let Thought-Aee Act iaction oonioseen---oeoeeooe tively. Then the agent's 's historical trajectory h at time step t can be denoted as:
loop be represented by (T, o), respec-
h (u, To, ao 00, TI, 011, 01, Tt- 0:-1: 0t-1).
(I)
Conditioned the history trajectory hú, the agent with parameters 0 will produce its next thought T.
and action Og according to the policy (T,, og(hy, and will receive an observation Og from the code interpreter after action a executed. The whole trajectory terminates either when the agent emits an answer or when a predefined maximum number of rounds T is reached. For simplicity, in the following sections, we denote the input part provided to the agent (including q. f. and d) as x and the trajectory (including answer a) sampled from the agent as To (-)x))
SCALING DATA- AGENT DATA
3.1 FILE COLLECTION AND QUERY SYNTHESIS
Data File Collection. First, we need a large amount of raw data files f to scale up the potential synthesized task volume. Fortunately, the Internet and the open community benchmarks already host a massive reservoir of such files. We first target Kaggle, which contains tens of thousands of csv and xxsxx spreadsheets. Using the official Kaggle API multiple domains, and then discard files that i) can not be loaded, ii) are extremely small (< 20 rows) or large (> 1, 000 rows), or dii) contain anomalous data types. After this pipeline, retain 3, 400 csv and 560 x x1sx files. For database files, we draw primarily from the training set of BIRD (Li al., 2023b) and OmniSQL (Li et al., 2025a), both of which high-quality corpora widely used in the Text-to-SQL field. Similarly, we sample from these sources and apply an analogous filtering pipeline, finally obtaining 1, 954 sqlite files.
crawl diverse subset of files spanning
Phttps:/ https:///www.//// www.wwwwwagaaaa.oo kaggle, com/ hom//occ//ppp
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Query Categorization and Synthesis. To generate specific queries, we devise an automated script to extract intaainfoomattin d of each data file, such as table headers, column names, data types, and representative rows, and then feed these metadata into DeepSeek-V. V3 (DeepSeck-AI, 2024) to synthesize queries q. To ensure both diversity and ime-grainedness of the generated questions, we refer to and refine the taxonomy in Wu al. (2025b) and classify the data analysis tasks into 18 fine-grained categories (see la). For each category, we carefully curate 4 ~ 6 exemplar queries that vary in complexity and domains and serve as few-shot demonstrations. Under the guidance of these type-specific contexts, every data file is used to generate diverse set of queries that span the full spectrum of the proposed taxonomy. To further elevate query complexity, we adopt a recursive easy-to-hard composition scheme that chains multiple task types, i.e., the output of one task is fed 5 times, we progressively amplify the difficulty and create
as input to the next. By iterating multi-hop analytic challenges that go well beyond the capability required by any single task type. The prompts for query synthesis can be found in A.p..1.2.
3.2 EXPERT TRAJECTORY SAMPLING AND FILTERING.
Knowledge Augmented Trajectory Sampling. To guarantee the quality of the synthesized trajecto- ries, we introduce knowledge weugeeee--ggeeeedd trajectory sampling framework. Initially, for each question category, we manually craft a high-level workflow that encodes procedural knowledge and steers the model during trajectory synthesis. To further boost answer quality, we impose self-consistency filter. We sample / independent trajectories per query and employ a judge model M powered by GPT-4o-mini (OpenAI, 2024b) to verify whether their final answers are consistent with reasoning rationales. Only trajectories that converge to the same answer are retained; among them, the judge model will also select the most concise and accurate one our training instance y/:
{c, 8, = M((w
( Toeyen (: (-Ik,
y =
y t WE none,
s=1
(2)
8= 0
where e the chain-of-thought process of the judge model to reach the binary conclusion of whether the sampled trajectories are consistent. We use DeepSeek-V33 policy model Tapeer During implementation, we set V = 3. The prompt used for trajectory sampling and the judge model M can be found in Appx.I3 and Appx.I.4, respectively. We extract the final answer from the trajectory as the final synthesized answer for the corresponding query g. However, this pipeline inherently biases us toward easier queries whose answers are more likely to coincide. To counteract this, we refine the high-level workflow knowledge k into more granular, step-by-step instructions for categories that exhibit low inter tite-trajectory consistency. Moreover, for trajectories that fail the consistency check, we feed the judge model's 's critique, prompting it to reflect and revise its reasoning path:
(DeepSeek-AI, 2025) as our expert
back to the agent as external
Whenected P. Teagon (-1k, {u W e),,),
c), if = 0.
(3)
The reflected trajectories {yfenected N will be fed into the judge model A again to conduct the consistency check and the trajectory selection in Eqn.2. This rescue loop not only sal salvages additional usable data but also enriches the diversity of thinking patterns embedded in the trajectory pool.
Rule-based Trajectory Filtering. In addition to discarding inconsistent trajectories, we apply three further rule-based filtering stages. 1) Format compliance. We drop any trajectory that deviates from the ReAct format, ensuring that every remaining trajectory can be losslessly converted into our target training schema. 2) Length control. We filter out trajectories whose final answer exceeds 1, .224 tokens, preventing the model from exploiting spurious hallucinations to artificially hit the correct string. 3) Linguistic integrity. We remove trajectories containing garbled text or intermingled natural languages, climinating samples that could destabilize the agent training. After the full filtering pipeline, we retain 11, 707 high-quality trajectories named as DATAMIND-1226
4 SCALING DATA-ANALYTI AGENT TRAINING
Dynamic Control Between SET and RL. In this paper, adopt combined paradigm of Supervised Fine- Tine-Tuning (SFT) and Reinforcement Learning (RL) for the agent training. Empirically, observe that it is difficult strike balance between the stages: the model needs to absorb
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sufficient knowledge from expert data during SFT. yet excessive imitation often rigidifies exploration during RL. Hence, following Zhang et al. (2025b), we employ a hybrid strategy that dynamically blends on-policy and off- off-policy learning, allowing the training procedure to flexibly trade off between exploitation of expert knowledge and continued exploration.
Given the training dataset D, express SFT loss
M
LSFT (0) - (z,,))
T(u o) log (o vet)
where Lyy & o) is an indicator function that masks any tokens produced by the environment feedback, ensuring that the model is optimized only on the agent-generated portion of the trajectory. For RL, we use the Decoupled Clip and Dynamic Sampling Policy Optimization (DAPO) (Yu et al., 2025) algorithm, minimizing the following function:
(0)
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s.t. 0 < (3 is sss_eeivvalen(y. (y.vi)} vi)} < G,
where { is group of G trajectories sampled from the agent policy Th and is the expert trajectory. Similar to SFT, any tokens emitted by the environment are discarded when computing the objective. (f) denotes the per-token i-th response, obtained by normalizing the group-level rewards {
saaaaaiii-s ratio, and Ar, is the advantage of the
No (vV. cx) vc,
A = Rg - mean (R) std( R )
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The inequality in Eqn.5 serves as a filtering criterion that discards trajectories lacking optimization utility whose rewards are uniformly 0 or uniformly prevent spurious gradient updates.
Finally, unlike the conventional SFT-then-RI. pipeline, we jointly optimize the agent by combining the SFT and RL objectives with dynamically balanced weighting factor:
C (0) (O) + (1 77 (DPOO())
where 7 E [0, 1] varies dynamically throughout training. In implementation, y is initialized to large value so that the agent first acquires knowledge from expert data via the SFT loss, and is then annealed to small value encourage extensive exploration through RL. Please refer $5.4 for our analysis of different settings. Importantly, for any trajectory that is filtered out by the inequality in Eqn.5, we will compute only the SFT loss. To increase the likelihood of producing eligible trajectories during the early stage of RL training, we perform cold start using DATAMIND- 12K before the process described above. We also analyze the effect of cold start for RL training $5.4.
Void Turns Filtering. In multi-tum agentic training, the model can experience distributional drift due to external feedback and multi-turn compounding errors during multi-turn rollout, which will easily result in trajectory collapse, thereby destabilizing RL training (Xue et al., 2025; Baronio et al., 2025; Mai et al., 2025). We also observe this phenomenon in our experiments. To stabilize training, we directly mask the entire loss contributed by trajectories that contain void turns. Here, a void turn is defined as an agentic loop that fails to produce a valid code snippet or answer.
Agentic Code-based Multi-turn Rollout. stable environment plays a key role stable on-policy RL training. In data-analytic agent training, massive concurrent file I/O and code execution can casily lead to environment crashes, especially with limited memory resources. To prevent this, we implement three optimizations: 1) Asynchronous interaction. We asynchronize model generation and code execution for different data samples, which can decouple peak GPU and CPU memory demands and avoid simultaneous file I/O and code-execution spikes. 2) Chunked code maintenance. We implement a liggh- notebook-style code generation strategy. The model only needs to produce the code snippet required for the current reasoning step, effectively reducing generation
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Published as a conference paper at ICLR 2026
Table I: Main Results. indicates that the original paper does report results for the corresponding model we use their official data code train the model for reproduction. denotes that we directly download
their official trained model for fair evaluation. The best results for each model group are highlighted in bold.
DABench
TableBench
BIRD
Avg.
Backbone
Method
pass
pass
pass 1
pass 3
pass
pass
@
69
Proprictary Models
GPT-4o
76.39
84.44
64.97
75.06
50.20
62.39
63.85
73.96
04-mini
79.12
71.03
57.04
DeepSeek-R1
78.73
87.55
68.96
79.52
55.80
66.17
67.83
77.75
DeepSeck-V3.I
81.32
89.49
72.52
81.68
57.89
68.12
70.58
79.76
GPT-5
78.21
85.21
69.93
78.37
60.17
65.19
99.44
76.26
Open-source
Qwen-2.5-Coder-32
73.15
81.32
61.11
72.26
41.20
60.17
58.49
71.25
QwQ-32B
70.17
85.21
57.79
75.19
50,30
64.21
59.42
74.87
ReAct
Llama-3.3-70B
69.78
80.16
55.47
70.36
59.10
61.45
73.03
Qwen-2.5-72B
75.33
86.38
65.44
76.21
60.30
67.02
77.36
React
15.05
35.41
11.70
28.63
7.02
18.71
11.26
27.58
L
36.71
71.98
41.01
70.36
11.99
16.75
29.90
Table-R
42.54
78.99
$6.36
63.61
10.69
36.53
52.03
Qwen-2.5
Coder-7B
OmniSQL:
26.46
36.19
39.95
50,25
57.11
66,30
41.17
50,91
SQL--I
24.90
34.63
40.84
50.64
56.78
66.23
40.83
50.50
DATAMIND
77.30
87.94
67.60
79.39
59.41
69.88
68.10
79.07
React
71.21
83.27
56.96
69.97
41.76
59,91
56.64
71.05
TableLLM
38.26
74.71
46.44
76.08
20.99
28.88
35.23
59.89
Qwen-2.5 Coder 14B
Table-R1
45.33
79.38
50.38
58.91
11.80
14.08
35.84
50.79
OmniSQL
26.46
39.30
41.98
52.67
58.80
67.41
42.41
53.13
SQL-RIF
27.24
40.47
41.22
51.02
58.02
66.62
42.16
52.70
DATAMIND
80.29
88.72
70.95
81.81
62.23
70.21
71.16
80.25
latency. Furthermore, whereas conventional notebook systems maintain global variable pool, which is memory-intensive, we retain only the textual code chunks. At runtime, we concatenate the active snippet with its predecessors, yielding the same global execution effect without the memory overhead. 3) Security Control. To ensure secure code execution, we isolate the runtime environment for each trajectory, enforce per-trajectory limits on CPU time and peak memory. and filter any snippet containing insecure function calls before execution. Additionally, we provide an automatic package-installation mechanism that dynamically checks and installs uninstalled Python packages.
Reward Design. Our reward mainly comprises three components: format reward Fformat- answer reward Tanswer: and length reward "ength. The agent is required to enclose its reasoning process within <think>..<</tiime
tags, place any generated data- proceprocessing code between <code> and </code>, and wrap its final answer in <answer>... <rnee>>..<//nswrre. The environment's execution results will be placed between <interpreter as many answers are descriptive and thus resist rule-based verification, we adopt model-as -uddgee powered by GPT-4o-mini (OpenAI, 2024b). We engineer dedicated LIM evaluation prompt detailed in A.p..I.4. Both rivenat and Tamswer are binary with only and 1. To mitigate the risk of the agent hacking the answer reward by hallucinating excessive tokens, we further impose length-based penalty to discourage overly verbose outputs. We define the length reward and the final reward as:
and < </interpreters
For the answer reward,
Nength
Tanswery
T Tanswer
1, Ium I 0.5,
I S Imin Imin < l Imax
R
F Forrat
1, Fanswer
Fllnngh
Inin
-0.5 0.5,
0.1,
Tioemat
=
0, Tanswer
0
Lmax (
(8)
We incentivize correct outputs. So long the predicted answer exactly matches the ground truth, the model will receive a high reward (2 0.5). The specific value is degendeepnndntt: we award a full reward if the answer length / is shorter than Imin it decays linearly to 0.5 when the length falls between Imin and l any sequence longer than Imux incurs a fixed length penalty of 0.5. According our observation, set Imin and /mxx 256 and 1024 respectively during experiments.

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Published as a conference paper at ICLR 2026
5 EXPERIMENTS
5.1 EXPERIMENTAL SETTINGS
Datasets and Metrics. We evaluate our model on three datasets related to data analysis: DABench (Hu al., 2024). TableBench (Wu et al., 2025b), and BIRD (Li al., 2023b). Our evaluation protocol aligns with our answer reward method, where a judge model powered by GPT-4o-mini (OpenAI, 2024b) is used to evaluate the correctness of the final answer. We report both pass @ and pass @3 3 scores for all the methods. Please refer to Appx.D D for more details.
Models and Baselines. We compare our models with five strong proprietary models and four outstanding open-source models (see Tab.1). In addition, we select four open- models that have been explicitly trained for data-analysis-rellted tasks: TableLLM (Wu et al., 2025b), Table-R1 (Wu et al., 2025c), OmniSQL. (Li et al., 2025a), and SQL.-RI (Ma al., 2025). We include Qwen- 2.5-Coder-7B and 14B (Hui et al., 2024) as our backbone models to compare different baselines. Detailed model information and reproduction protocols for all baselines are provided in Appx.E.
Training and Inference Setups. We use LlamaFactory (Zheng et al., 2024) for SFT training and verl (Sheng al., 2025) for RL training. For SFT, our learning rate is 1e 5 with warmup ratio of 0.0.1 and cosine decay schedule. Our global batch size is set to 16. For RL, we use a learning rate of le 6. The batch size is 32 with mini batch size of 4 and the number of epochs is 1. The rollout temperature 0.7, the top-p is 1.0, and the group size G is 4. We schedule & via cosine decay, annealing from peak of 0.9 0.95, and an inference batch size of 5 for all evaluations. For all the processes, the maximum number of interaction rounds T is set 10. Each of the training experiments can be run on machine with 80G A100 GPUs within 2 days. The detailed hyperparameter information can be seen in Appx.F.
valley of 0.05. At time, we fix the temperature 0.7, top-p to
5.2 MAIN RESULTS
As shown in Tab. I, our 7B model. DATAMIND-7B. achieves the best among all open-source models with an average score of 68.10%. Our 14B model, DATAMIND-145 14B, attains average score of 76.16% across all tasks, surpassing all proprietary mod- els (including the latest GPT-5 and DeepSSek--3.11 all open-source alternatives. Moreover, our DATAMIND series models demonstrate robust mastery of diverse data formats and exhibit balanced performance acToss all datasets. By contrast, specialized models degrade sharply when confronted with unseen data. For example, OmniSQL--B reaches 57.11 11% on BIRD, yet its performance on TableBench and DABench drops steeply. Note that to ensure a fair evaluation, we have converted all tables in these two benchmarks into sqlite files. Nevertheless, SQL-oriented models still underperform. This observation indicates the breadth of query types and file formats covered by DATAMIND-122 Furthermore, TableLLM and Table- RI are limited to small-scale tables. When evaluated on DABench's 's large-scale tables, they fail to generalize. and their accuracy deteriorates results highlight our model's capacity to handle complex tabular data, which can be attributed to the difficulty distribution embedded in DATA MIND-12K. Moreover, all trained baselines are exposed to significantly larger training corpora than ours (20K instances for TableLLM and Table-RL I, and 2.5M for OmniSQL and SQL-RI, versus only 12K for DATAMIND), yet we outperform them even on their adept benchmarks. This gain is attributable to the high high-quality reasoning trajectories curated in DATA- 12K and our stable training strategy. Our model also maintains high pass@) @3 score, preserving strong generation diversity while ensuring reliability.
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Figure 3: Ablation Data.
further on BIRD's multi-table analysis. These
5.3 ABLATION STUDIES
Ablation on Data Volume. We conduct ablation study the impact of training data scale by randomly sampling 2K, 4K, and 8K instances from DATAMIND-LI 12K to train the agent. The average performance of the 7B model on all the benchmarks is reported in Fig.3. It shows that the training
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non-con
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Figure 4: Analysis on Consistenny Filtering and Best Trajectory Selection. Con-select is our original setting, including self-consistency filtering and best trajectory selection by a judge model M. Non-select uses all the sampled trajeckories without the best selection. Random-select means randomly select trajectory instead of the best selection. Non-con directly leverages all the synthesized trajectories without self-consistency filtering.
data volume increases, the model's performance exhibits clear scaling law, demonstrating the scalability of the DATAMIND data-synthesis pipeline. Another intriguing observation is that the gap between pass@) and pass@) gradually narrows as the data size grows. This pattern seems to align with the conclusion in Yue et al. (2025a): RL training tends to increase the likelihood of generating correct samples, yet contributes little to expanding the model's coverage solvable problems.
Table Ablation Results Differ- ent Agent Training Strategy.
Ablation on Training Strategy. In Tab.2, we demonstrate the superiority of our hybrid SFT+RL training objective by comparing with pure SFT, zero-RI, and SFT-then-RI. on the 7B model. We need say that the majority of performance gains stem from our high-quality training data, as SFT alone raises the score from 11.26% (ReAct) 62.54%, substantially closing the gap with other strong models. However, zero-RL RL performs markedly worse than SFT, while SFT-then-RL yields only marginal gains over pure SFT. On the contrary, our hybrid training strategy can further boost the model performance to 68. 10% of pass@) and 79.07% of pass@3. What these numbers do not reveal is the training stability conferred by hybrid objective, as both zero-RL and SFT-then-RL can easily fall into collapse (detailed analysis about training stability can be found in $5.4), and we need run many trials and select relatively good checkpoint that performs best on the validation set.
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Ablation on Evaluation Method. Because the same judge model is used during both training and evaluation, to rule out the possibility that our model wins by hacking the judge, we re-score representative baselines and TableBench with Qwen-2.5-72B serving as an external judge, and plot the pairwise mapping between the judges in 5ig.5. As shown, the rankings produced by the two judges are virtually identical (each model-datased pair contributes two points, corresponding to pass @ 1 and pass @3). Quantitatively, the Pearson correlation between the two sets of scores is 0.96, indicating almost perfect linear relationship. Furthermore, we isolate the descriptive- generation tasks in TableBench and evaluate the outputs of DATAMIND and the baselines with the dataset's original objective metric, Rouge-I., in A.px.G.4. The resulting rankings again align closely with those produced by our model-as-judee Nevertheless, Rouge-L clearly fails to emphasizes surface lexical and sentence overlap with the gold label rather than answer correctness. Even the powerful DeepSeek- V attains very low Rouge-L score, underscoring the rationality and fairness of employing judge model for evaluation.
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Figure 5: Ablation Judge Model. Solid markers denote results DABench; hollow markers correspond to TableBench.
capture
model performance. It
5.4 ANALYSIS
Self-consistency filtering is more non-trivial than the best trajectory selection. In Fig.4, analyze the impact of the self-consistency trajectory filtering and best trajectory selection strategies through SFT the 7B model. It is evident that removing the self-consistency filtering (non-con)
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y=0.2
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Figure 6: The Influence of SFT Loss for RL Training. 7 = 0 denotes the absence of SFT loss, = 0.2 corresponds to a low SFT-loss weight, and dynamic 7y indicates our naive setting.
inflicts the most pronounced degradation model performance: both pass @1 1 and pass @3 drop to varying extents across all datasets. This observation suggests that the quality of the answers produced by a trajectory is a critical guarantee of the trajectory's overall quality. Provided that the final answers are consistent, we observe that randomly selecting a single trajectory for training is not necessarily worse than explicitly choosing the best one, and it even yields a clear improvement on DABench. We hypothesize that the judge model's 's preference bias may potentially reduce trajectory diversity. This conjecture
be further evidenced by the pass @3 scores of randon-select, which are on par with or superior to those of con-select across all three datasets. Moreover, the largest performance gains are obtained by including, without any selection, every trajectory that converges answer. This pattern holds across all datasets and indicates that the diversity of reasoning patterns and problem-solving strategies embedded in the trajectories is more beneficial to the model's 'ss reasoning capability, which aligns with the findings in Guha et al. (2025), although we cannot fully rule out the contribution of the larger training volume introduced by this unfiltered approach.
consistent
SFT loss is an effective stabilizer for RL training. When our experiments are still in an exploratory phase, we use DATA MIND-12K to examine how the weight of the SFT loss in Eqn.7 influences the RI. training on the 7B model without cold start. In Fig.6, we plot the dynamics of the answer reward across training steps under different y settings. As can be seen, when no SFT loss is imposed (y = 0), the answer reward declines almost monotonically. We attribute this failure two factors. First, the 7B model's ' limited multi-step reasoning capability makes it difficult to roll out high-quality trajectory groups for effective learning. Second, the heterogeneity of both data structures and code languages yields highly imbalanced trajectory distributions, resulting in unstable training. Raising 7 to 0.2 can alleviate the problem to some extent. The answer reward initially rises despite large oscillations, yet the SFT loss remains too weak to prevent the policy from eventually drifting away and collapsing. Under our dynamic-y schedule, the model first enjoys the stabilizing supervision of a strong SFT loss and after it matures, the SFT coefficient is gradually annealed to encourage exploration, yielding stable training during the whole process.
0=
SFT loss can also be the culprit of unstable training. Although SFT loss serves as an effective stabilizer for RI. training, we find that its persistent dominance through- out training can conversely trigger collapse. As shown in 7 fixing 7 at a high level also causes the answer reward to rise briefly, followed by a gradual decline. The underlying reason is that over-fitting to the SFT loss traps the policy in the rigid thinking patterns embedded in the expert trajectories, especially when these trajectories are synthesized from the same model, thereby crippling ex- ploration. To corroborate this, we track the entropy of the policy during training and observe pronounced entropy collapse phenomenon. In contrast, our dynamic-y strategy can keep the policy entropy consistently at relatively high level throughout training. Overall, we find the training pro- cess resembles raising a child. During early childhood, constant parental guidance (a large 7) is indispensable to keep the child from going astray. As the child grows up. excessive supervision stifles the child's innate drive for self-directed exploration. At that stage, judiciously letting go (a small 7) enables the child to discover their true capabilities through the feedback from the surrounding world.
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Figure 7: Answer Reward and Entropy
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RL can narrow the performance gap between different base models, but can hardly reverse the order. Fig.8 shows the impact of different degrees of cold start sub- sequent RI training. We randomly sample 3, 843 training data from DATA MIND-1KK (balanced on query types) and 240 test data (60 for each of the three test datasets) for evaluation. As the number of cold start training epochs increases, the marginal gain achieved by RL over the cold start checkpoint (i.e., the slope of the dashed line) dimin- ishes. This indicates that RL can narrow the performance gap between different base models (Liu et al., 2025). This phenomenon also aligns well between the 7B and 14B models with the same training epochs. Nevertheless, although the gap is narrowed, post-RI. performance remains positively correlated with the capability of the base model. This suggests that the bulk of knowledge is acquired during SFT, whereas RL primarily serves to unlock latent potential rather than explicitly push the model beyond its inherent capacity boundary (Yue et al., 2025a; Chu et al., 2025).
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Figure 8: Performance Gap Between Cold Start and RL with varying cold start epochs.
6 RELATED WORK
Agent Training. The earliest wave of LLM Agents (Wang et al., 2023; Xi et al., 2023) leverages the formidable reasoning capabilities of proprietary models (Qiao et al., 2023; Chen et al., 2025a; Yao al.. 2023; Zhou al., 2023; Hong et al., 2024: Li et al., 2023a; Wu et al., 2023). As AI entered the second half (Yao, 2025), numerous benchmarks targeting complex, domain-specific agentic tasks are introduced (Mialon et al., 2024; Phan et al., 2025; Jimenez al., 2024; Chan et al., 2025; Starace et al., 2025; Wei et al., 2025a), which expose the limitations of general-purpose agent architectures, elevating domain speciffc agent training to a critical necessity. The release of Large Reasoning Models (OpenAI, 2024c; DeepSeek-AI et al., 2025; Kimi et al., 2025) marks the triumph of Reinforcement Learning (RL) for LLMs. Consequently, a surge of work has sought to adapt RL algorithms to various agent domains (Jin al., 2025a; Song et al., 2025; Li et al., 2025c; Wu et al., 2025a; Feng et al., 2025; Qian et al., 2025; Li et al., 2025b). Yet these methods presuppose strong backbone model; researchers are therefore compelled to synthesize copious post-training data to compensate for the backbone's 's deficiencies. To the best of our knowledge, we are the first to systematically investigate the scaling of agent post-training in the data-analytic scenario, aiming to provide actionable insights for data synthesis and RL-driven training in other complex agent fields.
Aata-Analytic Agents and Benchmarks. Data Analysis Agents harness the reasoning capabilities and code-generation facility of LLMs to automate the end-to-end processing of data analysis tasks. Virtually all existing data analysis agents rely on closed-source models and are limited to prompt engineering. DS-Agent (Guo et al., 2024) incorporates human insights into data analysis tasks via case-based reasoning. AutoKaggle (Li et al., 2024) decomposes the data analysis pipeline into specialized sub-tasks through multi-agent architecture. Data-Copilot (Zhang et al., 2023) and AgenticData (Sun et al., 2025) stabilize agent behavior by orchestrating operations within predefined workflows. Data Interpreter (Hong et al., 2025) further enlarges the agent's exploration space by introducing dynamic graph-basc based workflows. To foster progress in this domain, numerous data analysis datasets have been introduced (Hu et al., 2024; Jing et al., 2025; Liu et al., 2024; Zhang et al., 2025a; Majumder et al., 2025). Nevertheless, each adopts its own task formulation and evaluation protocol, and the majority primarily rely on -nnaaattd labels. In this paper, we propose a fully automated pipeline to synthesize data analysis questions and executable code trajectories. Leveraging this synthetic corpus, we train two generalist data-analytic agents with advanced performance.
7 CONCLUSION
This paper introduces DATAMIND, a scalable data synthesis and agent training recipe designed to build generalist data-analytic agents. Built on DATAMIND, we curate DATAMIND-I2K, a high-quality training set that spans diverse task categories and data file formats for data-analytic tasks. Trained on DATAMIND- 12K, we obtain DATAMIND-7B and 14B, two advanced analytna agents with superior performance baselines. 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.
multiple benchmarks compared with various proprietary and open-source
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ETHICS STATEMENT
This study was conducted in full compliance with established ethical standards and research best practices. All data employed derived and synthesized exclusively from publicly available sources; no proprictary or confidential information was used. Every reference to these data sources is accurately and appropriately cited throughout the paper. We strongly encourage all users of our training dataset to uphold the highest ethical standards, ensuring fairesss, transparency, and responsibility in their research. Any use of the dataset that could cause harm or negatively impact society is strictly prohibited.
REPRODUCIBILITY STATEMENT
We have submitted all our training and evaluation code in the Supplementary Material. Due to OpenReview's file size limit, we only upload a 843 subset of DATAMIND-I2K. We will fully open DATAMIND-I2K and our models DATAMIND-7B and 14B immediately after the double blind review process. The detailed training data synthesis and agent training methods can be found in $3 and $4. We have clearly reported the details of our evaluation datasets and metrics in $5.1 and Appx.D. The detailed information about the models and baselines we use, including the model versions of proprietary models and the reproduction details of baseline models, can be found in $5.1 and Appx.E. The code framework used and the training and inference hyperparameters are mentioned $5.1 and Appx.F. All the prompts used in our paper are presented in Appx.I.
ACKNOWLEDGMENTS
We would like to express our sincere gratitude to the anonymous reviewers for their thoughtful and constructive feedback. This work was supported by the National Natural Science Foundation of China (No. 62576307, No. NSFCU23B2055, No. NSFCU19B2027), the Fundamental Research Funds for the Central Universities (226-2022 (023), the 2025 Zhejiang Provincial Center for Disease Control and Prevention Science and Technology Talent Incubation Project (No. 2025-A-04), the 2025 Zhejiang Health Informatics Association Scientific Research Program (Key Project. No. 2025XHZN- 201), titled "Research on Monitoring and Early Warning Methods of Al Large Model and Infectious Disease Epidemic Data Fusion",, undertaken by the Zhejiang Provincial Center for Disease Control and Prevention, the Yongjiang Talent Introduction Programme (2021A-156-G), and the Information Technology Center and State Key Lab of CAD&CG, Zhejiang University.
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A THE USE OF LARGE LANGUAGE MODELS
We affirm that Large Language Models are employed solely as an assisted tool to refine wording and sentence structure during our paper writing process. Their use in the experiments is strictly for scientific research purposes, and all such usage has been explicitly documented in our Experimental Settings and Reproducibility Statement. No other reliance on L.LMs is involved in this work.
B LIMITATIONS
This paper still has some limitations that must be acknowledged: a) At present, we only incorporate reasoning-oriente data-analysis tasks; training, predictive, and data-visualization tasks are deliber- ately excluded and reserved our important future work. b) Owing to computational constraints, our experimental backbone is restricted to the Qwen family, with model scale capped at 14B, Furthermore, not all mainstream benchmarks are covered in evaluation suite. c) Limited by computational resources, we have not exhaustively evaluated all RL training algorithms; moreover, data scarcity advanced RI.
constrains our RL runs to 350 steps. In future work, we will investigate
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strategies that enable stable, continual learning over substantially larger datasets. d) The current version of DATAMIND only accepts tabular data files and textual questions. In the future, we will extend DATAMIND additional modalities.
C A MORE DETAILED RELATED WORK
Agent Training. The earliest wave of LLM Agents (Wang et al., 2023; Xi et al., 2023) leverages the formidable reasoning capabilities of proprietary models (Qiao al., 2023; Chen et al., 2025a). At that time, researchers primarily boost agent performance through prompt engineering (Yao et al., 2023; Zhou al., 2023; Hong al., 2024; Li et al., 2023a; Wu et al., 2023). To equip open-source models with agentic skills, subsequent works introduce agent training (Chen et al., 2023; Zeng et al., 2024; Chen et al., 2024; Qiao et al., 2024) via SFT. Large-scale trajectory data, manually open-source
curated or synthetically generated by closed-source models, are used to instruct-tune models. As AI entered the second half (Yao, 2025), numerous benchmarks targeting complex, domain-specific agentic tasks are introduced (Mialon et al., 2024; Phan et al., 2025; Jimenez et al., 2024; Chan et al., 2025; Starace et al., 2025; Wei et al., 2025a), which expose the limitations of agent training
general-purposee agent architectures, elevating domain-spectie However, extensive studies have shown that SFT tends to drive agent models into paradigm overfitting, severely compromising their dynamic generalization ability in sophisticated agent scenarios (Chu et al., 2025; Jin et al., 2025b; Qiao et al., 2025).
critical necessity.
The release of Large Reasoning Models (OpenAI, 2024c; DeepSeek-Al et al., 2025; Kimi al., 2025) marks the triumph of Reinforcement Learning (RL) for LLMs. GRPO-style algorithms (Shao al., 2024; Yu al., 2025; Yue et al., 2025b; Wang et al., 2025a; Dong et al., 2025; Zhang et al., 2025b) enable models to autonomously explore while preserving robust generalization across diverse reasoning patterns. Consequently, a surge of work has sought to adapt GRPO-like algorithms to various agent domains (Jin et al., 2025a; Song et al., 2025; Li et al., 2025c; Wu et al., 2025a; Feng et al., 2025; Qian al., 2025; Li et al., 2025b; Wei et al., 2025b). Yet these methods presuppose strong backbone model; researchers are therefore compelled to synthesize copious post-training data to compensate for the backbone's s deficiencies in the target agent setting. To the best of our knowledge, we are the first to systematically investigate the scaling of agent post-training data and multi-turn RL training in the data-analytic scenario, aiming to provide actionable insights for data synthesis and RL-driven training in other complex agent fields.
Data-Analytic Agents and Benchmarks. Data Analysis Agents harness the reasoning capabilities and code-generation facility of LLMs to automate the end-to-end processing of data analysis tasks, constituting a critical component in the pursuit of autonomous scientific discovery (Chen et al., 2025b), Virtually all existing data analysis agents rely on closed-source models and are limited to prompt engineering. InfiAgent (Hu et al., 2024) pioneers the adoption of the ReAct (Yao et al., 2023) paradigm for tackling data analysis problems. DS-Agent (Guo et al., 2024) incorporates human insights into data analysis tasks via case-hased reasoning. AutoKaggle (I.i et al., 2024) decomposes the data analysis pipeline into specialized sub-tasks through a multi-agen architecture. Data-Copilot (Zhang et al., 2023) and AgenticData (Sun et al., 2025) stabilize agent behavior by orchestrating operations within predetined workflows. Data Interpreter (Hong et al., 2025) further enlarges the agent's 's exploration space by introducing dynamic graph-based workflows. To foster progress in this domain, numerous data analysis datasets have been introduced (Hu et al., 2024; Jing et al., 2025; Liu et al., 2024; Zhang et al., 2025a; Majumder et al., 2025; Wu et al., 2025b; Lei et al., 2025). Nevertheless, each adopts its own task formulation and evaluation protocol, and the majority primarily rely on human-annotate labels. In this paper, we propose fully automated pipeline to synthesize data analysis questions and exccutable code trajectorics. Leveraging this synthetic corpus, we train two generalist data-analytie agents with advanced performance.
D DATASETS AND EVALUATION DETAILS
We evaluate our model on three datasets related to data analysis. Here, we introduce the details and evaluation protocols for each dataset:
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DABench (Hu et al., 2024). DABench evaluates LLMs in data analysis tasks across 257 challenges from 52 CSV files, covering question categories. The original benchmark uses accuracy as the metric. The model'ss answer will be reformatted by LLM to specific structure and compared with the gold label using regular expression matching. Here, we directly utilize the model-as-judhe compare the predicted answer and the gold answer.
TableBench (Wu et al., 2025b). TableBench is real-world table reasoning benchmark spanning 18 fields and four major categories. The tables in TableRench are organized using json files. So we first transform them into csv files. Then, filter the trend forecasting and chart generation questions because these questions do not have explicit gold answers. The original benchmark uses Rouge-L as the metric, and we apply model-as-jutee -addeege instead.
BIRD (Li et al., 2023b). BIRD is a widely used Text-to-SQL benchmark. We use it to evaluate our model's ability to analyze table-based databases. Since the BIRD test set requires official leaderboard submission, we adopt its validation set as our testbed for convenience. As SQL execution typically returns structured and often very large tables that are hard for a model-as- judge to assess, we instead materialize each result into csv file and perform an exact match comparison against the gold label.
We adopt accuracy the final metric. For every model, we run three independent trials. We take the average score of the three trials as pass@1 1, while the union of the three trials is taken as pass @3 (i.e., success any single trial counts an overall success).
E BASELINES AND REPRODUCTION DETAILS
Models and Baselines. We compare our DATAMIND with five strong proprictary models: GPT-
4o (cpt-44-2024-080) (OpenAI, 2024a), 04-mini (o4-mini-2025-04-1
(OpenAI, 2025b), DeepSeek-R1 (deepseek-r1-2025- 8528) (DeepSeek-Al et al., 2025), DeepSeek- V3.1 (deepseek-v3 t -1-nothinnking) (DeepSeek-AL) 2025), GPT-5 (gp=-5-2025-0--111
(OpenAI, 2025a). We also include four outstanding open-source models: QwQ-32B (Qwen, 2025), (Hui et al., 2024), Llama-3.3-70B (Dubey et al., 2024), and Qwen-2.5-72B (Yang et al., 2024). In addition, tasks: TableLLM (Wu et al., 2025b) and Table-R1 (Wu et al., 2025c) are specialized for tabular reasoning, whereas OmniSQL (Li et al., 2025a) and SQL-RI (Ma al., 2025) are optimized for Text-to-SQL. generation. We include Qwen- 2.5nn-25-Cod and 14B (Hui al., 2024) as backbone models to compare different baselines. We use the Instruct version for all open-source models. Here we introduce the baselines we compare with and our reproduction details:
0wen-2.5-Coder-322
select four open-source models that have been explicitly trained
for aatta-analysis-reaatee
REACT (Yao et al., 2023). For untrained models, including all proprictary models and open-source models, we test them using REACT agent format in a zero-shot manner. The detailed prompt is the same with the prompt in Fig.9.
TableLLM (Wu et al., 2025b). TableLLM is trained on TableInstruct (Wu et al., 2025b), the training set of TableBench, with SFT. The data volume of Tablelnstruct is 19,661. We directly use TableInstruct train Dwen2.5-Coders
and 14B for reproduction. As TableInstruct has three prompt modes (i.e., TCoT, SCoT, PoTT), we test all of them and report the best results.
Table-R1 (Wu et al., 2025c). Table-R1 applies a -enhance SFT followed by a table-aware aware GRPO (Shao et al., 2024) training. The training data is also from TableInstruct for both SFT and RL stages. We follow the same data split and the training pipeline of Table-R1 to train Qwen2.5- 5- Coder-7B and 14B for reproduction. As TableInstruct has three prompt modes (i.e., TCoT, SCoT, PoT), we test all of them and report the best results.
OmniSQL (Li et al., 2025a). OmniSQL is trained on a large-scale synthesized text-to-SQL dataset SynSQL-2.5M with a data volume of 2.5M. Since it already has Qwen2.5-Coder-T7 and 14B version models, we directly use them for evaluation.
SQL-R1 (Ma et al., 2025). SQL-RI is further trained on OmniSQL with RL. The training data for RL is sourced from a 5K subset of SynSQL-2.5M, Since already has open-source Qwen2.5- Co and 14B version models, we directly use them for evaluation.
Note that for a fair comparison, we strictly adhere to the original input- output formats and benchmark configurations of every baseline. Specifically, for TableIL.M and Table-R1. 1, they are both trained
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Table 3: Detailed hyperparameters used in our paper.
Stage
Hyperparameter
Value
Global
max number of rounds T
10
learning rate
le-5
scheduler type
cosine
warmup ratio
0.1
SFT
batch size
16
training epoch
3
cutoff length
8192
learning rate Ir warmup style Ir warmup steps batch size mini batch size training epoch max prompt length max response length clip ratio low Flow clip ratio high Ehigh rollout temperature rollout topp rollout group size G scheduler type
le-6
constant
20
32
4
1
2048
8192
0.2
SFT+RL
0.28
0.7
1.0
cosine
y peak value y valley value
0.9
0.05
length reward Iinn
256
length reward mmx
1024
temperature topp
0.7
Inference
0.95
batch size
5
on TableInstruct, we evaluate them with the same prompt used in TableInstruct and converted each table file from all the benchmarks into the string representation required by their task settings before inserting it into the prompt. For OmniSQL and SQL-RI, which are both trained on Text-to-SQL tasks, we follow their native prompts and ask the models to generate complete SQL statements directly based on the table schema and the user query. In addition, we uniformly transform all benchmark data files into the sqlite format via scripts to match their task requirements.
F TRAINING AND INFERENCE DETAILS
We use LlamaFactory (Zheng et al., 2024) for SFT training and verl (Sheng et al., 2025) for RL training. For SFT, our learning rate is 1e with warmup ratio of 0.1 and cosine decay schedule. Our global batch size is set to 16. For RL, we use a learning rate of le with mini batch size of 4. The rollout temperature 0.7, the top-p is 1.0, and the group size G is 4. We schedule via cosine decay, annealing from a peak of 0.9 to a valley of 0.05. At test time, fix the temperature to 0.7, top-p 0.95, and inference batch size of 5 for all evaluations. For all the processes, the maximum number of interaction rounds T is set to 10. Each of the training experiments can be run on machine with 8 80G A100 GPUs within 2 days. The detailed hyperparameters e in DATAMIND are presented in Tab.3.
The batch size is 32
G MORE RESULTS
G.1 MORE BASE MODELS.
We primarily conduct experiments with the Qwen family, as it exhibits stronger reasoning capacity and higher plasticity during RL training (Gandhi et al., 2025; Wang et al., 2025b). To verify the universality our DataMind pipeline on different kinds of base models, we additionally train 1-ma-3.1-88--nnsteel (Meta, 2024) DATAMIND-- 14K and the results
widely acknowledged that Qwen
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reported in Tab.4. It can be observed that the DATAMIND pipeline transfers robustly across model families. Llama-8B 8B not only surpasses Llama-70B but also performs on par with strong baselines. Its pass @3 further approaches the level of DeepSeek- V3.1 and DATAMIND-7B.
Table 4: Results of Llama-8B Model compared with other baselines.
DABench
TableBench
BIRD
Avg.
Backbone
Method
pass@
p
pass
pass@
pass
pass
pass @1
pass @
DeepSeck-V3.1
81.32
89.49
72.52
81.68
57.89
68.12
70.58
79.76
(PPT5
78.21
85.21
69.93
78.37
60.17
65.19
69.44
76.26
3.3-7B Qwen-2.5-72B
69.78
80.16
55.47
70.36
59.10
68.58
61.45
73.03
ReAct
75.33
86.38
65.44
76.21
60.30
69.49
67.02
77.36
Diven2.5-Codeer
DATAMIND
77.30
87.94
67.60
79.39
59.41
69.88
68.10
79.07
Llama-3.1-88-nnttautt
DATAMIND
73.67
87.16
64.49
76.46
58.52
69.56
65.56
77.73
G.2 MORE BASELINES.
Data Interpreter has been embedded into the MetaGPT framework' as an off-the-shelf analytics tool. Despite our best effort decouple its pipeline and it in isolation, reproduce the reported 94.93% GPT-4o score on DABench and it also cannot be adapted to SQL tasks. The same discrepancy has been independently acknowledged in another paper (You al., 2025). Our reproduction results are shown in Tab.5. It can be found that task-specifi scaffolds do not consistently outperform the vanilla ReAct paradigm, and prompts engineering for one model may fail to generalize to others. We believe that the greatest truths are the simplest. This is also why we believe agent training, rather than prompt engineering and scaffold constructing, is the more promising path.
are unable to
Table 5: Results of Data Interpreter compared with other baselines.
DABench
TableBench
Backbone
Method
pass@
pass@ 3
pass@
pass 3
DeepSeck-VV
ReAct
81.32
89.49
72.52
81.68
Data Interpeeter
67.70
80.93
61.98
74.52
Qwen-2.5-72B
ReAct
75.33
86.38
65.44
76.21
Data Interpreter
62.26
75.10
49.90
58.84
RoAct
15.05
35.41
11.70
28.63
Q,en-2.55 C.---ode--75
Data Interpreter
54.09
65.37
27.99
41.82
MANA
77.30
87.94
67.60
79.39
RoAct
71.21
83.27
$6.96
69.97
Qwen-2.5-Coder--44
Data Interpreter
60.70
74.71
40.71
55.82
DATA
80.29
88.72
70.95
81.81
G.3 MORE BENCHMARKS.
To demonstrate that DATAMIND can generalize to more challenging data- aataa-nnllsis benchmarks, fur- ther evaluate on QRData (Liu et al., 2024), a dataset tasks and re- lies heavily on domain commonsense knowledge. We compare DATA and DATA 14B agents against the strongest open-source and closed- source models; results are reported in Tab.6. Our 14B model still achieves the best performance, benefiting from the comprehensive coverage of data- analysis types and domains in 122, as well as the consistent performance gains brought by our stable agent training strategy. Our 7B model also delivers competitive results. However, constrained by parameter count, it lacks the domain-specife knowledge required, which prevents it from achieving equally impressive performance.
Table 6: Results on QRData.
QRData
Backbone
Method
pass@I
pass
that features intricate
DeepScok-V3.1
ReAct
60.75
75.67
Qwen-2.5.77
ReAct
60.50
72.75
Qwen-2.5-7B
DATAMIND
57.66
69.34
Qwen-2.5-14B
DATAMIND
62.04
77.62
Phtpps: : thub. cot.cound//tttttto onAgents Soannntts/MeGP.P.
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G.4 MORE EVALUATION METRICS.
Table 7: Rouge-L Results various methods compared with our model-as-judge.
To further validate the soundness of our model-as- judge protocol, we re-score the descriptive tasks in TableBench with Rouge-L, and contrast these scores with the ratings previously assigned by GPT-4o-mini. The comparison is reported in Tab.7. The result- ing rankings align closely with those produced by our model-as-judge- Nevertheless, Rouge-L clearly fails to capture true model performance. It over- emphasizes surface lexical and sentence overlap with the gold label rather than answer correctness. This renders the performance differences among models marginal. Even the powerful DeepSeek-VS. attains a very low Rouge-L score, underscoring the rationality and fairness employing judge model for evaluation.
TableBench
Backbone
Method
Rouge-L
pass@
DeepSeek-VE5
19.64
72.52
Qwen-2.5-72B
ReAct
17.93
65.44
TableLLM
11.33
9.81
Qwen-2.5-7B
Table-RI
13.86
25.49
DATAMIND
23.41
76.47
H DATA CONTAMINATION ANALYSIS
Table Table Header Overlap Ratio between DATAMIND and all the benchmarks.
To the best of our knowledge, the DABench data files harvested from GitHub, whereas the tables TableBench are extracted from Wikipedia, with neither overlapping with the corpora we curate. We sample the training split of BIRD and adopt syn- thetically generated schemas provided by OmniSQL. Consequently, our sources are also disjoint from the BIRD test set. To quantify this claim, we compute the header-overlap ratio between every table in DATAMIND-122 and the headers of each benchmark in Tab.8. The resulting overlap is exactly 0% (or statistically indistinguishable from 0%) for every benchmark, demonstrating that DATAMIND- 12K introduces no data contamination into the evaluation.
DABench
TableBench
BIRD
DATAMIND
1.29%
0.00%
0.02%
I PROMPTS USED IN OUR PAPER
1.1 TRAINING AND EVALUATION PROMPT
Training and Evaluation Prompt
You are an expert-level data analyst and statistician who solves any data challenge through rigorous logic, systematic planning, and deep investigation. Your primary task is to answer user questions by analyzing the provided data source. You can solve the given problem step by step by utilizing Python code execution (for CSV files) or SQL queries (for database files) to support your reasoning.
Prohlem-Solving Protocol
You should think through the problem logically, outlining your reasoning process in <think> and </think> tags.
After reasoning, write the appropriate code exccute your plan. Place your code between
<code> and </code> tags.
For CSV files and Excel files, write Python code using libraries like pandas, numpy, skleam, etc. to analyze the data. The format should be:
'python
<your python code here>
...
For database files, you should only use the 'get_db, idd_innf function view the database structure information and exxeuueesqql function to execute sql queries, and save the results. Example:
'pythhn
get_db_info()
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or ''yython
execute_sql(sq/E valid SQL. query here", output_path="resule
The execution results will returned <interpreter> and </interpreter> tags. Every time you get the code execution result, you must conduct reasoning and analyze these results carefully between <think> and </think>. the result indicates an error or unexpected behavior, explain the issue and rewrite the previous step code. If the result indicates the code ran successfully, analyze whether the original problem has been fully solved.
it has been solved, explain your reasoning and then provide the final answer wrapped in <answer>..<</nnwer>>
If not, continue reasoning and provide the next step of code based on your previous correct code.
4. Whenever you're 're confident about the result, you can directly provide your answer to the question inside <answer>... </answer>. <ansser...<annswr>.
For CSV files and excel files, you should directly provide your answer. Make it concise and to the point. (c.g. <answer>The final answer is 3, </a/swewer>>)
For database files, you must tell me the file name of the result CSV file. (e.g. <answer>The final answer is saved in the CSV file named 'result.csv' </answer>)
# CSV File and Excel File Analysis Notes
Load data from 'dtta/iile'' directory using the specified CSV or Excel filename. Temporary files can be saved to 'data/tmp'.
In your first step, you should use print() to inspect the data columns, the first rows, and the type of the columns and so on to understand the data structure.
If you want to get the value of variable your code, you must print it out using print() (e.g., print(f" max: ((max] }}") ())) to understand the current value and state of variables.
Only proceed to the next step of code the current step is written correctly. Each step must build the previous code.
# Database File Analysis Notes You can only use sqlite database engine to execute your SQL queries. In your first step, use get_db_ing _db_inf(() inspect the database schema. 3. In your answer, you must provide the file name of the result CSV file. Make sure the answer file has been saved the current directory.
Additional Notes:
1. Avoid including irrelevant commentary outside of the designated tags <think>, <code>, <interpreter>, and <answer>.
2. If the last step is not correct, should first conduct deep analysis of the previous step and then rewrite the code to fix the issue.
3. Keep your responses concise, structured, and directly tied to the original question.
# Data Source
**The data source path is (data_source_path)
Figure 9: Prompt for Training and Evaluation.
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1.2
QUERY SYNTHESIS PROMPT
Query Synthesis Prompt
You are a data analysis expert and assistant. Your task to generate high-quality, insightful data analysis questions based given data file's metadata, headers, and column descriptions. You always consider the semantics of the data and produce questions that can support exploratory data analysis, business understanding, or hypothesis gen- eration. Your output should be clear, structured, and directly usable for data analysis planning.
# Objective
Based on the information from the data file and the specified question type, generate one unique and meaningful exploratory data analysis (EDA) question. The phrasing and structure of the question must closely follow the style of provided examples be- low. The goal is to generate questions that can reveal various types of insights from the dataset.
## Here is meta information about the data file:
### Description: ddescription.replacee '###")
### Header: (meta_infol) heedd")]
### Columns: meta colummcs
### Columns Type: laa_jnnnooo'tpa
### Columns Range: meta infol "roo[rraeee]] ']]
### Columns Unique Values: [meta_infof
### Row Number: [meta_infof row ]o
### Column Number (meta_infof infol' collccc_ooouunn
## EDA Question Types with Short Descriptions
1. ** ""ggregation" ** Summarize data values to understand overall patterns or trends, such as calculating averages, totals, or maximums. Often used quantify general behavior across dataset.
#* **Ranking*** Identify and compare items based on specific metrics to determine their relative standing, often highlighting the highest, lowest.
**Counting** Determine the number of items that meet specific conditions or criteria. This typically involves filtering data and counting matching entries.
*Cooparisonn* Compare values across data points to identify differences, similarities, extremes, often focusing identifying the highest, lowest, range between values.
5. **Domain Specific** Analyze data within a specific field or context using domain knowledge interpret results, answer specialized questions, derive insights meaningful to
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that area.
6. **Causal Analysis** Analyzing relationships beyond correlation, often requiring controlled experiments or advanced statistical methods to infer causality.
**Satisticaal Analysis** Apply statistical measures (e.g., median, standard deviation, variance, growth rate) summarize, describe, or evaluate patterns and variabilityy within the data.
7.
**Correlation Analysis** Measure the strength and direction of relationship between two quantitative variables, typically using correlation coefficient assess how closely the variables move together.
9. ** Arithmetic Calculation Perform basic mathematical operations (e.g., addition, subtraction, multiplication, division) to compute totals, differences, or projections based on the given data.
10. **Decciptive Analysis** Provide an overview of the dataset by explaining its structure, key columns, and any observable patterns trends. Focus on summarizing what the data shows without drawing causal inferences.
11. **Impact Analysis** Analyze relationships between variables to determine how one factor influences another. This involves identifying trends, correlations, or causations within the data assess impact over time or across categories.
12. F*ct Checking** Retrieve and verify multiple related facts across different data points to answer question. This requires connecting and cross-referencing information from various parts of dataset.
13. ** Anomaly Detection** Identify data points that significantly differ from the expected pattern norm, potentially indicating errors, outliers, or unusual behavior. 14. **Multi-hop Numerical Reasoning**
combining multiple pieces of information Perform numerical reasoning that requires calculations or logical sequencing to reach the final answer.
steps. This often involves intermediate
15. **Time-based Calculation** Analyze data across time periods to identify trends, changes, or cumulative values, often involving comparisons between different time intervals or calculating growth rates over time.
16. **Ditributtin Analysis** Analyze how data values are distributed for given variable or across groups. This includes assessing normality (e.g.. via the Shapiro- Wilk test), skewness, kurtosis, comparing distributions between groups using statistical tests (e.g.. the Mann-Whitnee U). helps understanding the shape, spread, and symmetry of the data, and whether it meets assumptions required for other analyses.
17. **Feature Engineering** Create, transform, combine, or extract variables to enhance data quality modeling potential. This includes generating new columns, deriving ratios or indicators, aggregating related values, or reformatting data to reveal deeper patterns or prepare for predictive analysis.
18. **Coppreeensive Data Preprocessing** Perform a sequence of data cleaning and preparation steps ensure the dataset is ready for analysis modeling. This includes handling missing values, transforming data types, encoding categorical variables, normalizing or scaling numerical features, and correcting inconsistencies. The goal to produce clean, well-structured, and analysis-ready dataset.
## Analysis Question Type Focus
### Primary Question Type:
The question should primarily focus on the given type: (question_infog infof question_type""
## Requirements:
Return exactly one data analysis question per execution.
The question should mainly focus on the specified question type:
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[question_iniss question_type") Use clear, concise, and practical language suitable for data analysts.
"The question should be grounded in the context and structure of the data file.
**Thh phrasing and style of the question must closely mirror the examples provided**. This includes using similar formats, tones, and syntactic patterns.
**Follow the same linguistic conventions as the examples** For example, if the examples use formulations like "What is the difference between..." "How many.."" ", or "Which of the following...", ", then your generated question must follow similar patterns to ensure stylistic consistency.
**Output format must include**:
The question inside cruuettion...</qusstiinn
A short description inside
The question type inside "<type>..<iyye>>"
## Template Enforcement
You are strictly required follow the templates provided for the question type below: _ree__quettionteeee serprooeeeelllltuuutioooon jttppueetttooooooo__ee infoo'question___ "nvestion_template" }
You must use the template to generate the corresponding question. Generate the question by using the template with relevant columns, values, or descriptions from the dataset. Do not introduce new question styles, structures, or alternative phrasings.
This is hard constraint, not suggestion.
## Here are some examples: {question_ines infol' qquestionexamplee
## Now, generate high-quality EDA question:
Figure 10: Prompt for Query Synthesis.
1.3 TRAJECTORY SAMPLING PROMPT
Trajectory Sampling Prompt
You are Data Analysis Assistant who can solve the given problem step by step with utilizing code execution tool to support your reasoning.
You should think through the data analysis problem logically, outlining your rea- soning process in the <rassoings...</resonnng tags.
2. After reasoning, you write Python code if necessary and use print() state- ments to inspect key values to support your reasoning. Remember to place your Python code inside <code> "python.... "<'coode>> tags. You may use libraries like pandas, numpy, sklearn, etc.
User will execute the code and return results in <intepreterx...chhterrreeee Every time you get the code execution result, you must conduct reasoning and analyze these results carefully between <reasoning> and </reasoning>, following this process: Whether the result indicates error or unexpected behavior, explain the issue and rewrite
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the previous step code, or the result indicates the code successfully, analyze whether the original problem has been fully solved.
it has been solved, explain your reasoning and then provide the final answer wrapped in <answer>...<answer
If not, continue reasoning and provide the next step of code based on your previous correct code.
4. Whenever you're confident about the result, you can directly provide your an- swer to the question inside <answer>..</anwwer>
5. Format Example:
Code Format:
<reasoning>
Your reasoning here step by step, explaining your thought process and how you will approach the problem.
</reasoning>
<code>
'python
Your Python code here, using print() statements to inspect variables.
</code>
Answer Format:
<reasoning>
Your final reasoning here. Summarize the solution to the question. </reasoning>
<answer>
Your final answer to the question, keep your answer as brief as possible while ensuring it is complete, concise, and clearly stated. **No longer than 1024 words. **
</answer>
6. Important Notes:
read data files, load from the 'file dirr using the file [question]] filename']]".
**If you want to get the value of variable in your code, you print it out using print()
(e.g., print(f"max: { (mma))]")*** to understand the current value and state of variables. Try use one or two print() statements one single step to avoid overwhelming the user with too many outputs.
Don't tt visualization libraries like matplotlib or seaborn, the user will not be able to see the plots.
Only proceed to the next step of code if the current step written correctly. Each step must build the previous code.
Avoid including irrelevant commentary outside of the designated tags <reasonings, <code>, <interpreter>, and <answer>.
Keep your responses concise, structured, and directly tied to the original question.
7. When beginning to solve a problem, you are encouraged to follow these two ini- tial steps general guideline (not rigid rule):
SSeep I: Load the Excel file specified the question using pandas.read_endd _xceeeecce from the path (file_dir) __ddir/(quustionf "frrfiomn"iileemmm"""""""" Then, inspect the dataset structure printing:
the first 3 rows using df.hhad(33)
the list of column names using df.columns
optionally, the data types using df.dttpes if helpful for understanding column types.
Step Analyze the question to determine which columns or rows in the dataset are relevant.
Then apply appropriate operations to those columns/rows to address the problem. For complex tasks, you may adapt or break second step into smaller sub-tasks and solve
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them incrementally. This approach helps manage complexity and ensures accurate reasoning throughout the analysis.
Error Handling: If your code contains a bug raises an exception during execution, first focus on debugging and resolving the issue before proceeding with solving the main problem.
fget sew.sssssotqustiionn queigueeettiioniqqtetttiiin question_type (_pee_tyyp])]
Figure 11: Prompt for Trajectory Sampling.
1.4
JUDGE MODEL PROMPT
Judge Model Prompt (Self-consistency)
You are a precision evaluation system. Your task is to determine whether three Al Al-generated answers equivalent and fully correct. Use the following evaluation criteria:
Semantic Equivalence: For descriptive questions, all answers must express the same meaning, even if phrased differently.
Numerical Agreement: For numerical/calculation questions, any numeric values must be within 3% of each other. Convert all units to be comparable before comparing. If a number is derived through formula, check that the process is logically sound.
3. Completeness: The final answer must fully address all aspects of the original question. Do not ignore implicit sub-questions.
4. Source Traceability: Identify which answer (1, 2, or 3) most clearly and accurately represents the final synthesized answer.
Respond using:
creasoning...c/rrasooiii Please this tag to think through the evaluation process step by step, explaining your reasoning and how you will choose the best answer. Be careful to consider all three answers.
cooorecc.../ccorrectts Use "yes' or "no" to indicate whether all three answers are semantically equivalent and fully correct. It's fine if the differences are justifiable. <number>...cnumberr Use 2, or 3 to choose the single best answer that should be used as the final response. If you cannot determine clear best answer, choose the one that most complete and accurate.
#*# Question question
### Answer answer_ 1
### Answer answer_2
### Answer 3
answer 3
Evaluate standard:
1. Are all three answers semantically equivalent and/or numerically consistent (53% difference)?
2. Do all three answers fully resolve the original question without omissions?
3. Which single answer is the best basis for a final polished response?
Respond in this format: <reasonigg3...ccreaonnig
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Published as a conference paper at ICLR 2026
<correct>yes</corree>>>>> nuunber..<<numberr>
<correeD<no</correet>
Figure 12: Prompt for Consistency Judge Model.
Judge Model Prompt (Reward)
You are fair and professional evaluator. Your task is to assess how closely an Al assistant's answer matches the provided ground truth for a given question. You are to provide numerical score for how well the response answers the question based the ground truth answer.
Your evaluation should focus the assistant's answer to the question. Begin your evaluation by comparing the assistant's 's answer with the ground truth answer. Identify and correct any mistakes. as objective as possible.
Evaluate the correctness (0 for incorrect, for correct) the predicted answer the question:
Question: {question]
Predicted answer: (pred_amee answer)
Ground truth answer: (ground_truth)
Rules for judgment:
For numerical questions, any result within 3% of the ground truth answer is considered correct. Please compare abs(Predicted unswer)abs(Truu answer) with 3% to make your decision.
2. For multiple-choice questions, exact match is required.
The answer should clear and complete.
4. The calculation process alone is not considered correct.
Wrap your reasoning inside <thought></hhughhe and wrap the accuracy score in- side <score></<core> tags. Keep your reasoning concise, no more than 3-5 clear and informative sentences. Avoid repetition unnecessary elaboration. Only output the reasoning and score using the required tags. Follow the output format as shown in the example below:
<thought>The predicted answer is 115624, which exactly matches the ground truth. The relative error is O, well within the 3% threshold. The answer is clear, correct, and directly responds to the question.</thought <score>1</score
Figure 13: Prompt for Reward Judge Model.
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Reviews:
[{"review_id": "Lxch7OgWJ2", "reviewer": "Reviewer_dfWg", "summary": "This paper proposes a pipeline for constructing generalist data-science agents, addressing key challenges such as data scarcity, unstable multi-turn rollout, and balancing RL and SFT training. The authors curate a 12K-sample dataset and train models on it. The resulting DATAMIND-7B/14B models show promising performance on several data-analytic benchmarks.", "questions": "1. Since gold answers are already available, why use a judge model for evaluation? Could this introduce noise, and how might that be mitigated?\n2. Can DATAMIND handle multimodal inputs (e.g., CSV + plots), or is it limited to text-based tabular data?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The paper is well-motivated, and the proposed pipeline is comprehensive, combining a thoughtful data-synthesis process with a robust training strategy. The experimental results are promissing and the authors also analyze useful empirical insights into agent training.", "weaknesses": "1. Missing baseline comparisons: The paper does not include comparisons with other data-analysis agents such as DS-Agent or Data-Interpreter, which would strengthen the empirical claims.\n2. Limited base model and benchmark diversity: The model is trained only on Qwen, which may introduce family bias and inflate the reported performance. Moreover, evaluation is restricted to three tabular/SQL-focused benchmarks; the generalization to broader data-analysis scenarios remains unclear.\n3. Additional studies on different base models, dataset sizes, and the impact of recursive composition depth would make the analysis more comprehensive.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "RGJ1UVmkAR", "reviewer": "Reviewer_H48n", "summary": "This paper addresses three key challenges in developing data analysis agents:\n(i) the scarcity of training data resources,\n(ii) the instability of long-horizon agent training and the unclear trade-off between SFT and RL training stages, and\n(iii) the complexity of memory management in parallel agentic rollouts and multi-turn code generation.\n\nTo tackle these challenges, the authors propose:\n(i) constructing a specialized training corpus,\n(ii) dynamically combining SFT and RL losses with an adaptive weighting scheme, and\n(iii) introducing an asynchronous agent generation and code execution framework with a chunk-wise code maintenance strategy to reduce peak memory usage.", "questions": "Given the results in Table 1, it seems there is a clear upper bound on the performance across all models on the multiple benchmarks listed in the table. Do the authors have any insights into why this could happen and how to further improve the performance?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The idea of building open-source data-analytic agents is interesting and timely.\n2. The proposed system demonstrates promising results across multiple benchmarks.", "weaknesses": "1. It is unclear whether the observed performance improvements primarily stem from the proposed training methodology or from the curated dataset. A clearer ablation isolating these factors would strengthen the claim.\n2. The benefit of dynamically combining SFT and RL remains ambiguous. From the rightmost subfigure in Figure 4, the training process appears to continuously shift from SFT to RL, although the paper does not follow an “SFT-first-then-RL” paradigm. It is not evident whether this dynamic approach yields tangible advantages over the “SFT-first-then-RL” paradigm. An ablation study is needed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "NzPx57ZZez", "reviewer": "Reviewer_dp72", "summary": "The paper presents DATAMIND, a data synthesis and training pipeline for building generalist data-analytic agents that operate through code-based multi-turn ReAct loops over heterogeneous files (.csv, .xlsx, .sqlite). The recipe includes: an 18-category task taxonomy with recursive easy-to-hard composition for query generation; knowledge-augmented trajectory sampling with self-consistency filtering and rule-based checks; a dynamically weighted SFT+RL objective (DAPO) with void-turn masking; and a memory-frugal rollout framework with sandboxed execution.", "questions": "Please specify total RL budget: number of episodes, average steps per episode, total generated tokens, and wall-clock/compute (GPU type, days). The paper mentions ∼350 RL steps; what exactly constitutes a “step” here?\n\nFor the dynamic weighting γ, provide the full schedule, per-step values, and an ablation where γ is learned or adapted from validation signals instead of cosine.\n\nHow do SFT and RL gradients interact on overlapping tokens when a trajectory passes the RL filter? Are environment-emitted tokens always masked in both losses, and is there any credit assignment to code vs. natural-language tokens?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "End-to-end, scalable pipeline. The work integrates dataset harvesting, fine-grained taxonomy, compositional query generation, trajectory vetting, and stable multi-turn rollout, offering a complete path from raw files to trained agents suitable for community reuse.\n\nTraining design that addresses common failure modes. The dynamic SFT+RL weighting, void-turn masking, and asynchronous chunked execution directly target instability in code-based multi-turn RL and are supported by ablations on reward/entropy dynamics.\n\nStrong empirical results with broad format coverage. Consistent gains across three benchmarks and multiple file modalities, including conversion stress tests (e.g., TableBench/BIRD to unified formats), suggest robustness beyond narrow table QA.", "weaknesses": "Evaluation and reward rely on model-as-judge. Using GPT-4o-mini for both training rewards and final evaluation introduces circularity and potential stylistic bias; limited exact-match or verifier-based checks reduce measurement rigor, especially for descriptive answers.\n\n\nPotential data overlap and protocol discrepancies. Training on BIRD/OmniSQL-adjacent corpora and converting evaluation benchmarks to new formats risk contamination or distribution shifts; fairness of comparisons may be affected by enforcing a single ReAct prompt across heterogeneous baselines.\n\n\nScope and generality claims are ahead of evidence. Results are confined to reasoning-oriented analytics; visualization, forecasting, and end-to-end scientific workflows are excluded. Claims of outperforming top proprietary models would benefit from additional third-party evaluators and held-out, verifier-checkable tasks.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "AabCf839CQ", "reviewer": "Reviewer_xdfR", "summary": "This paper introduces DATAMIND, a framework for training open-source data-analytic agents. The paper tackles three major challenges:\n\n- **Insufficient data resources:** proposes a fine-grained task taxonomy and a recursive query synthesis pipeline to generate diverse, multi-turn analytic problems, coupled with a knowledge-augmented, self-consistency-filtered data curation process that produces the DATAMIND-12K dataset.\n\n- **Training strategy:** designs a hybrid SFT + RL objective with dynamic weighting to balance imitation and exploration during training.\n\n- **Unstable multi-turn rollouts:** develops a memory-efficient rollout mechanism with asynchronous interaction and chunked code management to improve reliability in multi-turn, code-based roll-outs.\n\nFine-tuned with DATAMIND, a ReAct-style agent with Qwen-2.5-Coder-14B backbone achieves state-of-the-art results (average pass@1 = 71.16 across three benchmarks including DABench, TableBench, and BIRD), outperforming both open-source and proprietary baseline models including DeepSeek-V3.1 and GPT-5.", "questions": "1. For Rule-based Trajectory Filtering, especially 1) Format compliance and 3) Linguistic integrity, were these checks also performed using LLM-as-judge, or implemented through e.g. regular expression?\n\n2. How is the format reward computed?\n\n3. In table 1, Qwen-2.5 Coder-14B fine-tuned with TableLLM and Table-R1 perform worse on TableBench than the pretrained Qwen-2.5 Coder-14B (ReAct). Is there any insight on why fine-tuning on TableInstruct led to worse performance?  \n\n4. Regarding Fig 6, my understanding is that the conclusion “RL can narrow the performance gap between different base models” is based on models with the same backbone but different in SFT epochs (e.g., Qwen-7B with 1 v.s. 2 epochs). Could this conclusion extend to base models of different sizes (e.g., Qwen-7B v.s, Qwen-14B)?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. **Comprehensive framework:** the paper proposes a unified framework that integrates training data synthesis and SFT + RL fine-tuning for training open-source data-analytic agents, offering a reproducible recipe for scaling open-source data-analytic models.\n\n2. **Strong empirical performance:** with DATAMIND, Qwen-2.5-Coder-14B model outperforms all baseline proprietary and open-source models, while Qwen-2.5-Coder-7B model achieves performance comparable to GPT-5.\n\n3. **Comprehensive analyses:** the paper provides insightful ablation studies that offer practical understanding in curating high-quality training data and stabilizing the training procedure.", "weaknesses": "1. Since part of the training data is sourced from Kaggle, is there a possibility that the training data may overlap with public benchmarks used for evaluation? Some discussions or analysis to rule out such possibility would further enhance the validity of the results.\n\n2. All expert trajectories are sampled from DeepSeek-V3.1, and GPT-4o-mini serves as the judge during both trajectory filtering and reward modeling. This raises concerns about introducing potential biases from these two models, and some discussions on how to mitigate such biases would be very helpful.\n\n3. LLM-as-judge with GPT-4o-mini is used to evaluate the performance, although for some benchmarks objective metrics are available (e.g. Rouge-L for TableBench as described in Appendix D). This raises concerns about the reliability of the evaluation, especially when GPT-4o-mini is also used as the judge during training trajectory curation and reward modeling. Including objective metrics results would improve the robustness of the evaluation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)