Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training

In three linesTabGRAA, a group-relative advantage alignment method, improves tabular language models through iterative reward-guided post-training. Across five benchmarks, it outperforms adapted DPO, KTO, and NPO baselines, optimizing the fidelity-utility-privacy trade-off beyond supervised fine-tuning alone.

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