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

Yunbo Long, Tejumade Afonja, Guangya Hao +2

Tabular language models can generate synthetic tables by modeling rows as token sequences, but they are typically trained once with supervised fine-tuning and then used as static s…