Pith. sign in

REVIEW 3 major objections 5 minor 7 cited by

A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This survey organizes RL-based and generative approaches to tabular feature selection and generation into one taxonomy and argues they have complementary trade-offs.

desk verdict A readable, well-organized survey of a narrow subfield, but the review's coverage is heavily tilted toward the authors' own research line and its comparative claims rest on assertion rather than evidence. read the letter →

arxiv 2502.08828 v2 pith:IEJTJ6GW submitted 2025-02-12 cs.LG cs.AI

Wangyang Ying , Cong Wei , Nanxu Gong , Xinyuan Wang , Haoyue Bai , Arun Vignesh Malarkkan , Sixun Dong , Dongjie Wang
show 2 more authors
Denghui Zhang Yanjie Fu
This is my paper
classification cs.LGcs.AI
keywords tabulardatadata-centricAIfeatureselectiongenerationreinforcementlearninggenerativeautomatedengineeringlargelanguagemodels
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey argues that the many scattered methods for automatically improving tabular data can be seen as two coherent families: those that use reinforcement learning to search over discrete feature choices, and those that use generative models to optimize in a continuous embedding space. It organizes these families into a taxonomy covering both feature selection and feature generation, and it compares their strengths and weaknesses across performance, interpretability, adaptability, and automation. A reader would care because automated feature engineering is the main route to better machine learning on tabular data without manual effort; a clear map tells practitioners when to use RL-based methods, when to use generative methods, and when to combine them. If the taxonomy holds, it gives researchers a shared language for positioning new methods and identifies concrete open problems, from privacy-preserving feature engineering to LLM-based and multimodal feature generation.

What carries the argument

The central object is the taxonomy itself, grounded in two named mechanisms. Reinforcement learning treats feature selection and generation as a Markov decision process: an agent selects features or applies transformation operators, receives a reward from the downstream model, and iteratively refines its policy. Generative models use an embedding-optimization-generation loop: observed feature sets are encoded into a continuous latent space, the space is searched by gradient-based optimization, and new feature decisions are decoded from the optimized embedding. The encoder-decoder-evaluator architecture appears repeatedly as the concrete implementation of the generative paradigm, with long-range dependencies captured by transformer-based variational autoencoders and redundancy controlled by orthogonality constraints.

What would settle it

A systematic literature search followed by a benchmark would settle it: if methods outside the surveyed set populate categories the taxonomy lacks, or if on a fixed collection of tabular datasets the RL-versus-generative performance ranking contradicts the survey's stated trade-offs, the central claim is falsified.

Watch

Extended reading notes

Core claim

The paper's central claim is that the state of data-centric AI for tabular learning is best understood through a two-axis map: one axis is the task (feature selection versus feature generation), and the other is the optimization paradigm (reinforcement learning versus generative modeling). On the RL side, feature selection and generation are cast as sequential decision processes driven by reward signals; on the generative side, features are embedded into a continuous space where selection or construction is found by gradient-based search and then decoded. The survey further claims that these two paradigms have complementary trade-offs: RL offers traceable decision paths and adaptivity to streaming data but suffers from high computational cost and sensitivity to reward design, while generative methods enable smoother high-dimensional search and higher automation but bring black-box interpretability and dependence on training data quality. It completes the picture with practical selection criteria, hybrid strategies, and a list of open challenges that follow from the comparison.

Load-bearing premise

The load-bearing premise is that the papers selected for review—many written by the same author group—are representative of the broader field of RL- and generative-based tabular feature engineering; if the selection is one-sided, the taxonomy and the strengths-and-limitations comparison could be distorted.

Editorial extensions

If this is right

  • If the taxonomy is correct, new RL-based or generative feature-engineering methods can be positioned by which cell they fill, and practitioners can choose approaches by task type and data characteristics.
  • The comparative analysis implies that RL-based methods should be preferred for dynamic, streaming, or sequentially changing data, and generative methods for static high-dimensional datasets with ample unlabeled structure.
  • The survey's hybrid scenario suggests a concrete architecture: a generative model proposes a wide pool of candidate features, and an RL agent selects and refines them, balancing exploration with long-term rewards.
  • The stated future directions indicate that LLM-based feature generation and multimodal integration are the next frontier, with open questions about tabular encoding and cross-modal alignment.
  • If the identified limitations are taken seriously, research priority should shift to reward design for RL and to making generative feature engineering interpretable and privacy-preserving.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The RL-versus-generative divide in this survey looks like a special case of a broader spectrum between discrete combinatorial search and continuous relaxation; the same trade-off likely applies to other data-centric tasks such as data cleaning, imputation, and augmentation.
  • The emphasis on the embedding-optimization-generation paradigm suggests a testable extension: applying the same continuous-space approach to feature selection in non-tabular modalities, such as graph or time-series data, might outperform RL baselines on tasks with high-dimensional feature spaces.
  • A practical benchmark could decide the comparative claims: on a fixed set of public tabular datasets, measure RL-based versus generative feature engineering under a fixed compute budget; if their relative performance reverses between tasks, the survey's guidance would need to be conditioned on more than data dynamics and dimensionality.
  • The survey implies that interpretability is a key differentiator, but post-hoc interpretability tools could narrow that gap; a hybrid pipeline that uses RL for selectivity and generative models with surrogate explanations could serve both goals.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This survey reviews reinforcement learning (RL) and generative approaches for feature selection and feature generation in tabular data-centric AI. It proposes a taxonomy in Figure 1, reviews methods in Sections 3 and 4, compares the two families qualitatively in Section 5, offers practical strategies in Section 6, gives selection guidance in Section 7, and discusses challenges and future directions in Section 8. The paper's central claim is that RL-based and generative techniques can be organized into the proposed taxonomy and usefully compared in terms of performance, interpretability, adaptability, and automation.

Significance. If the taxonomy and comparative conclusions were established, the survey would provide a useful structured map of an emerging area and would help practitioners choose between RL-based and generative feature engineering. The paper offers a clear conceptual framing, especially the contrast between RL's discrete sequential search and generative methods' continuous embedding-space optimization. However, the survey's evidentiary base is currently too narrow and self-referential to support the claim of a systematic review. The load-bearing assumption that the cited papers are representative of the field is not justified by any stated selection protocol, and the qualitative comparisons are not grounded in empirical evidence. The manuscript would be a valuable contribution after a major revision that addresses these structural issues.

major comments (3)
  1. [§1, Figure 1; Sections 3–4] The survey's core claim is that it 'systematically reviews existing feature selection and generation techniques' (Section 1), but the manuscript gives no search strategy, database list, time span, inclusion/exclusion criteria, or screening procedure. The method papers discussed in Sections 3–4 are overwhelmingly from the same author group or immediate collaborators: of the roughly 25 works reviewed, about 20 are by Liu, Fan, Wang, Ying, Gong, Xiao, and coauthors. As a result, a reader cannot verify that the taxonomy in Figure 1 and the qualitative comparisons in Section 5 are representative of the broader field rather than a description of one research trajectory. This is a structural limitation of the central argument, not an allegation of misconduct; it is fixable by adding a transparent literature-selection protocol and expanding coverage to independent work.
  2. [Figure 1; Sections 3.1–4.2] The proposed taxonomy is not consistently defined. Figure 1 places methods under branches such as 'Two-Step Generative Approaches', 'Cascading Frameworks', and 'Hybrid And Specialized RL Approaches', but the text never defines 'two-step', and several methods appear to fit more than one branch: Xiao et al. [2023] is presented in Section 4.1 as a generative method but 'leverages reinforcement learning' for data collection; Wang et al. [2024a] is classified under generative feature generation yet its title is 'Reinforcement-Enhanced Autoregressive Feature Transformation'. Because the central contribution is the map itself, overlapping categories and unstated criteria for branch assignment need to be clarified.
  3. [§5; §7] The comparative claims in Section 5 and the guidance in Section 7 are stated as established findings, but no empirical evidence, benchmark table, or cited source is provided for statements such as 'RL-based methods: RL-based methods offer better interpretability', 'Generative-based methods: ... more stable than RL in some cases', and 'Generative models ... automation level is higher than RL'. These are plausible hypotheses, but the paper does not distinguish them from documented results, and some claims are internally qualified later (e.g., deep RL models are admitted to become harder to interpret). The authors should either ground the comparisons in a systematic synthesis of reported experimental results or explicitly label Section 5 as design considerations/opinion.
minor comments (5)
  1. [§2] The sentence 'improving model performance, efficiency, and interoperability' appears to use 'interoperability' where 'interpretability' is meant; this should be corrected.
  2. [§6] The citation 'Bai et al.' in the discussion of differential privacy has no year or venue and is not listed in the references; the bibliographic entry should be completed.
  3. [§8] The phrase 'This chapter explores' should be 'This section explores', since the manuscript is organized into sections, not chapters.
  4. [§2] The feature generation example '[f1, f2] → [f1/f2, f1 − f2, f1+f2/f1]' is ambiguous; parentheses such as '(f1+f2)/f1' would avoid implying f1 + (f2/f1).
  5. [References] Several reference entries are incomplete or inconsistently formatted: 'Sutton [2018]' is listed as a book without the full title formatting, and 'Kamatchi and Uma [2025]' has inconsistent capitalization; a careful reference-checking pass is needed.

Circularity Check

1 steps flagged · score 5.0 of 10

The survey's claimed field map is built substantially from the authors' own corpus: ~19 of ~24 method papers in the Figure 1 taxonomy and §§3–4 are same-group citations with no disclosed selection protocol, so the 'systematic review' and the §5/§7 comparative guidance largely reduce to that self-selected body, though the cited papers are peer-reviewed.

  1. self citation load bearing [Abstract; Figure 1 (Section 1); Section 3.1–4.2 (surveyed method set); Section 5 (comparative conclusions)]
    "Abstract: "We systematically review existing generative methods for tabular data engineering, analyzing their latest advancements, real-world applications, and respective strengths and limitations." Section 3.1: "Existing works can be categorized into three-fold: Multi-Agent RL Frameworks, Single-Agent RL Frameworks, and Hybrid and Specialized RL Approaches." Figure 1: "Multi-AgentRL •Group-Wise Method [Liu et al. [2019]] •Advanced Statistical Summaries & GCNs State Representation [Liu et al. [2021a]]; •Enhanced Reward Scheme [Fan et al. [2021b]].""

    The survey's central deliverable is the Figure 1 taxonomy and the §3–5 comparative analysis of RL-based versus generative tabular feature methods. Roughly 19 of the ~24 substantive method papers populating Figure 1 and §§3–4 share authorship with the present paper (Liu et al. 2019/2021a/2021b; Fan et al. 2020/2021a/2021b; Zhao et al. 2020; Wang et al. 2022/2024a/2024b; Xiao et al. 2023/2024; Ying et al. 2023/2024a–d; Gong et al. 2024a/2024b; Hu et al. 2024).

full rationale

This paper is a literature survey, not a derivation: it contains no equations, no fitted parameters, and no empirical predictions, so the by-construction circularity patterns (self-definitional identities, fitted-input-called-prediction, uniqueness theorems) do not apply. The relevant circularity question is whether the survey's central deliverable — the Figure 1 taxonomy and the RL-versus-generative comparison of §§3–7 — is supported by independent evidence or reduces to the authors' own research program. The reference list shows that roughly 19 of the ~24 substantive method papers reviewed (Liu et al. 2019, 2021a, 2021b; Fan et al. 2020, 2021a, 2021b; Zhao et al. 2020; Wang et al. 2022, 2024a, 2024b; Xiao et al. 2023, 2024; Ying et al. 2023, 2024a–d; Gong et al. 2024a, 2024b; Hu et al. 2024) carry Yu-jie Fu group authorship or immediate-group collaboration, and the taxonomy categories are named after that group's own paradigm arc. The abstract's claim to 'systematically review existing generative methods' is therefore not distinguishable, on the paper's own face, from a summary of one research trajectory: no search strategy, inclusion/exclusion criteria, or quality screen appears anywhere in the manuscript. The §5 strengths/limitations and §7 selection guidance are generalizations over that same corpus. I do not escalate to 6+ for two reasons. First, the self-cited method papers are peer-reviewed at KDD, ICDM, SDM, TKDE, CIKM, and NeurIPS and are benchmark-tested, so they are independent evidence about what those specific methods do, per the citation-independence rule. Second, the central claim that RL and generative techniques are distinct, powerful frameworks for tabular feature engineering also rests on independent general references (Sutton 2018; Ruthotto and Haber 2021) and a small number of genuinely external method papers (Zhu et al. 2022; Zhang et al. 2024a, 2024b; Huang et al. 2024; Xiao et al. 2025). The verdict is therefore partial: a pervasive, load-bearing self-citation pattern that undermines the completeness and representativeness of the survey's map and comparative guidance, without a by-construction reduction of any equation or fitted result.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This is a review paper, so there are no fitted parameters or new entities. The main assumptions are about the representativeness of the surveyed literature and the validity of the qualitative comparisons.

assumptions (3)
  • domain assumption The surveyed papers are representative of the field of RL-based and generative tabular feature engineering.
    The survey does not describe a systematic literature search or inclusion criteria; the reference list is heavily weighted toward the authors' own work (reference list, Figure 1).
  • domain assumption Feature selection and feature generation are the two core tasks of data-centric AI for tabular data.
    This framing is asserted in Section 1 and shapes the entire taxonomy, but no evidence is offered that these are the only or primary tasks.
  • domain assumption The qualitative strengths and limitations in Section 5 are generally valid.
    Claims such as 'RL-based methods offer better interpretability' and 'generative methods are more stable' are presented without empirical benchmarks.

how reviews work

0 comments
Cite this review

Pith. "Pith review of A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective." pith.science (2026). https://pith.science/paper/IEJTJ6GW

@misc{pith2026250208828,
  author       = {Pith},
  title        = {Pith review of: A Survey on Data-Centric AI: Tabular Learning from Reinforcement Learning and Generative AI Perspective},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IEJTJ6GW}},
  note         = {Machine review of arXiv:2502.08828}
}
read the original abstract

Tabular data is one of the most widely used data formats across various domains such as bioinformatics, healthcare, and marketing. As artificial intelligence moves towards a data-centric perspective, improving data quality is essential for enhancing model performance in tabular data-driven applications. This survey focuses on data-driven tabular data optimization, specifically exploring reinforcement learning (RL) and generative approaches for feature selection and feature generation as fundamental techniques for refining data spaces. Feature selection aims to identify and retain the most informative attributes, while feature generation constructs new features to better capture complex data patterns. We systematically review existing generative methods for tabular data engineering, analyzing their latest advancements, real-world applications, and respective strengths and limitations. This survey emphasizes how RL-based and generative techniques contribute to the automation and intelligence of feature engineering. Finally, we summarize the existing challenges and discuss future research directions, aiming to provide insights that drive continued innovation in this field.

Figures

Figures reproduced from arXiv: 2502.08828 by the authors.

Figure 1
Figure 1. A taxonomy overview of RL-based and generative techniques in tabular data-centric AI. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Feature selection and feature generation are formulated as [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Generative AI captures feature knowledge in a continuous [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Discussion (0). Sign in to comment.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DELTA: Variational Disentangled Learning for Privacy-Preserving Data Reprogramming

    cs.LG 2025-08 reject novelty 6.0 of 10

    DELTA uses reinforcement learning to find useful feature transformations, then a disentangled variational autoencoder to generate transformed features that keep task utility while reducing sensitive-attribute predicti...

  2. Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback

    cs.LG 2025-05 conditional novelty 6.0 of 10

    Reinforcement learning fine-tuning with numerical fitness rewards improves equation discovery accuracy and noise robustness of a pretrained symbolic regression transformer.

  3. Brownian Bridge Augmented Surrogate Simulation and Injection Planning for Geological CO$_2$ Storage

    cs.RO 2025-05 conditional novelty 6.0 of 10

    A Brownian bridge augmented framework improves surrogate simulation accuracy and injection plan quality on synthetic CO2 storage datasets compared with established baselines.

  4. Sculpting Features from Noise: Reward-Guided Hierarchical Diffusion for Task-Optimal Feature Transformation

    cs.LG 2025-05 conditional novelty 6.0 of 10

    DIFFT generates task-optimal feature transformations via reward-guided latent diffusion with a semi-autoregressive decoder, outperforming ten baselines on 14 tabular datasets.

  5. Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives

    cs.LG 2025-06 reject novelty 5.0 of 10

    TimesCLIP aligns image-based and text-based views of the same time series via contrastive learning to improve forecasting accuracy on several benchmarks, but the full multimodal model is not used on two of the six lon...

  6. Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories

    cs.LG 2025-05 conditional novelty 5.0 of 10

    A router-selector-generator LLM agent team with offline PPO and dual memories unifies feature selection and generation, reporting improved downstream performance on six tabular datasets.

  7. LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation

    cs.LG 2025-06 conditional novelty 4.0 of 10

    A product-of-experts decoder that blends a fine-tuned LLM's token probabilities with a gradient-searched sequence decoder produces more valid and stable feature transformations than either alone.

Reference graph

Works this paper leans on

16 extracted references · 12 canonical work pages · cited by 7 Pith papers

  1. [2023]

    Topology-aware reinforcement feature space reconstruc- tion for graph data

    Wangyang Ying, Haoyue Bai, Kunpeng Liu, and Yanjie Fu. Topology-aware reinforcement feature space reconstruc- tion for graph data. arXiv preprint arXiv:2411.05742 ,

    arXiv

  2. [1]

    Feature interaction aware automated data representation transformation

    Ehtesamul Azim, Dongjie Wang, Kunpeng Liu, Wei Zhang, and Yanjie Fu. Feature interaction aware automated data representation transformation. In Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) , pages 878–886. SIAM,

    work page 2024

  3. [3]

    Evolutionary large language model for automated feature transformation

    Nanxu Gong, Chandan K Reddy, Wangyang Ying, and Yan- jie Fu. Evolutionary large language model for automated feature transformation. arXiv preprint arXiv:2405.16203,

    arXiv

  4. [5]

    An empirical analysis of feature engineering for predictive modeling

    Jeff Heaton. An empirical analysis of feature engineering for predictive modeling. In SoutheastCon 2016 , pages 1–6. IEEE,

    work page 2016

  5. [6]

    Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy

    Xiaohan Huang, Dongjie Wang, Zhiyuan Ning, Ziyue Qiao, Qingqing Long, Haowei Zhu, Min Wu, Yuanchun Zhou, and Meng Xiao. Enhancing tabular data optimization with a flexible graph-based reinforced exploration strategy. arXiv preprint arXiv:2406.07404,

    work page Pith review arXiv

  6. [8]

    Efficient reinforced fea- ture selection via early stopping traverse strategy

    Kunpeng Liu, Pengfei Wang, Dongjie Wang, Wan Du, Dapeng Oliver Wu, and Yanjie Fu. Efficient reinforced fea- ture selection via early stopping traverse strategy. In 2021 IEEE International Conference on Data Mining (ICDM) , pages 399–408. IEEE,

    work page 2021

  7. [9]

    Knockoff-guided feature selection via a single pre-trained reinforced agent

    Xinyuan Wang, Dongjie Wang, Wangyang Ying, Rui Xie, Haifeng Chen, and Yanjie Fu. Knockoff-guided feature selection via a single pre-trained reinforced agent. arXiv preprint arXiv:2403.04015,

    arXiv

  8. [10]

    Beyond Discrete Se- lection: Continuous Embedding Space Optimization for Generative Feature Selection

    Meng Xiao, Dongjie Wang, Min Wu, Pengfei Wang, Yuanchun Zhou, and Yanjie Fu. Beyond Discrete Se- lection: Continuous Embedding Space Optimization for Generative Feature Selection . In 2023 IEEE Interna- tional Conference on Data Mining (ICDM) , pages 688–

    work page 2023

Show all 16 references
  1. [11]

    Self-optimizing feature generation via cat- egorical hashing representation and hierarchical reinforce- ment crossing

    Wangyang Ying, Dongjie Wang, Kunpeng Liu, Leilei Sun, and Yanjie Fu. Self-optimizing feature generation via cat- egorical hashing representation and hierarchical reinforce- ment crossing. In 2023 IEEE International Conference on Data Mining (ICDM), pages 748–757. IEEE,

    2023

  2. [13]

    Tfwt: Tabular feature weighting with transformer

    Xinhao Zhang, Zaitian Wang, Lu Jiang, Wanfu Gao, Pengfei Wang, and Kunpeng Liu. Tfwt: Tabular feature weighting with transformer. arXiv preprint arXiv:2405.08403,

    arXiv

  3. [14]

    Dy- namic and adaptive feature generation with llm

    Xinhao Zhang, Jinghan Zhang, Banafsheh Rekabdar, Yuanchun Zhou, Pengfei Wang, and Kunpeng Liu. Dy- namic and adaptive feature generation with llm. arXiv preprint arXiv:2406.03505,

  4. [15]

    Simplifying reinforced feature selection via restructured choice strategy of single agent

    Xiaosa Zhao, Kunpeng Liu, Wei Fan, Lu Jiang, Xiaowei Zhao, Minghao Yin, and Yanjie Fu. Simplifying reinforced feature selection via restructured choice strategy of single agent. In 2020 IEEE International conference on data min- ing (ICDM), pages 871–880. IEEE,

    2020

  5. [2020]

    Difer: differentiable automated feature engineer- ing

    Guanghui Zhu, Zhuoer Xu, Chunfeng Yuan, and Yihua Huang. Difer: differentiable automated feature engineer- ing. In International Conference on Automated Machine Learning, pages 17–1. PMLR, 2022

    2022

  6. [2021]

    Autogfs: Automated group-based feature selection via interactive reinforcement learning

    Wei Fan, Kunpeng Liu, Hao Liu, Ahmad Hariri, Dejing Dou, and Yanjie Fu. Autogfs: Automated group-based feature selection via interactive reinforcement learning. In Pro- ceedings of the 2021 SIAM International Conference on Data Mining (SDM), pages 342–350. SIAM,

    2021

  7. [2024]

    Neuro-symbolic embedding for short and effective fea- ture selection via autoregressive generation

    Nanxu Gong, Wangyang Ying, Dongjie Wang, and Yanjie Fu. Neuro-symbolic embedding for short and effective fea- ture selection via autoregressive generation. arXiv preprint arXiv:2404.17157,

    arXiv

  8. [2025]

    Deep fea- ture synthesis: Towards automating data science endeav- ors

    James Max Kanter and Kalyan Veeramachaneni. Deep fea- ture synthesis: Towards automating data science endeav- ors. In 2015 IEEE international conference on data sci- ence and advanced analytics (DSAA) , pages 1–10. IEEE,

    2015

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.