Table Question Answering (TableQA) benefits significantly from table pruning, which extracts compact sub-tables by eliminating redundant cells to streamline downstream reasoning. However, existing pruning methods typically rely on sequential revisions driven by unreliable critique signals, often failing to detect the loss of answer-critical data. To address this limitation, we propose TabTrim, a novel table pruning framework which transforms table pruning from sequential revisions to gold trajectory-supervised parallel search. TabTrim derives a gold pruning trajectory using the intermediate sub-tables in the execution process of gold SQL queries, and trains a pruner and a verifier to make the step-wise pruning result align with the gold pruning trajectory. During inference, TabTrim performs parallel search to explore multiple candidate pruning trajectories and identify the optimal sub-table. Extensive experiments demonstrate that TabTrim achieves state-of-the-art performance across diverse tabular reasoning tasks: TabTrim-8B reaches 73.5% average accuracy, outperforming the strongest baseline by 3.2%, including 79.4% on WikiTQ and 61.2% on TableBench.

翻译：表格问答（TableQA）显著受益于表格剪枝，后者通过消除冗余单元格来提取紧凑的子表格，从而简化下游推理。然而，现有的剪枝方法通常依赖于由不可靠的评判信号驱动的顺序修订，往往无法检测到对答案至关重要的数据丢失。为解决这一局限，我们提出了TabTrim，一个新颖的表格剪枝框架，它将表格剪枝从顺序修订转变为黄金轨迹监督的并行搜索。TabTrim利用黄金SQL查询执行过程中的中间子表格推导出黄金剪枝轨迹，并训练一个剪枝器和一个验证器，使逐步剪枝结果与黄金剪枝轨迹对齐。在推理过程中，TabTrim执行并行搜索以探索多个候选剪枝轨迹并识别最优子表格。大量实验表明，TabTrim在多样化的表格推理任务中实现了最先进的性能：TabTrim-8B达到了73.5%的平均准确率，优于最强基线3.2%，包括在WikiTQ上的79.4%和在TableBench上的61.2%。