Self-Improving Tabular Language Models via Iterative Group Alignment
Summary: arXiv:2604.18966v1 Announce Type: cross
Abstract
While language models have been adapted for tabular data generation, two fundamental limitations remain: (1) static fine-tuning produces models that cannot learn from their own generated samples and adapt to self-correct, and (2) autoregressive objectives preserve local token coherence but neglect global statistical properties, degrading tabular quality. Reinforcement learning offers a potential solution but requires designing reward functions that balance competing objectives — impractical for tabular data. To fill the gap, we introduce TabGRAA (Tabular Group-Relative Advantage Alignment), the first self-improving framework for tabular data generation via automated feedback.
Key Features of TabGRAA
At each iteration, TabGRAA employs an automated quality signal to enhance the generation process. The key features include:
- Automated Quality Signal: Utilizes classifiers or distance-based rewards to categorize generated samples into high- and low-quality groups.
- Group-Relative Advantage Objective: Reinforces realistic patterns while penalizing artifacts, ensuring higher quality in generated data.
- Modular Signal Choice: The specific quality signal can be adjusted, providing flexibility and adaptability in the framework.
- Continuous Feedback Cycle: Quality signals are recalibrated using newly generated samples, establishing an ongoing improvement loop.
- Data-Leakage Mitigation: The model fine-tunes solely on self-generated signals, reducing exposure to real records and enhancing privacy.
Advancements in Tabular Data Generation
TabGRAA represents a significant leap forward in tabular data synthesis. Traditional methods often rely on static statistical replication, which fails to adapt to new data. In contrast, the dynamic nature of TabGRAA allows for:
- Improved Fidelity: The ability to generate data that closely mirrors real-world distributions.
- Enhanced Utility: The generated tabular data can be used more effectively in downstream applications.
- Privacy Preservation: By limiting exposure to original datasets, it ensures compliance with data protection regulations.
Experimental Results
Experiments conducted with TabGRAA demonstrate its superiority over existing methods in terms of fidelity, utility, and privacy. Notably, it matches or even surpasses diffusion-based synthesizers, marking a pivotal advancement in the field of tabular synthesis.
Conclusion
In conclusion, TabGRAA sets a new standard for self-improving tabular data generation. By leveraging automated feedback and a modular approach to quality assessment, it evolves beyond traditional static models, offering a robust solution for generating high-quality tabular data. This innovation not only enhances the quality of generated datasets but also significantly improves their applicability in various domains.