# In-Table Evidence Selection

The authors propose TabSieve, a select-then-predict framework in which, given a table and a query row, the model first selects a small set of informative rows as evidence and then predicts the missing target conditioned on that evidence; to enable this capability they build TabSieve-SFT-40K by synthesizing high-quality reasoning trajectories from 331 real tables with a strong teacher model and strict filtering, and introduce TAB-GRPO, a reinforcement learning recipe that jointly optimizes evidence selection and prediction correctness with separate rewards and stabilizes mixed regression and classification training via dynamic task-advantage balancing; on a held-out benchmark of 75 classification and 52 regression tables, TabSieve consistently improves performance across shot budgets, with

The authors propose TabSieve, a select-then-predict framework in which, given a table and a query row, the model first selects a small set of informative rows as evidence and then predicts the missing target conditioned on that evidence; to enable this capability they build TabSieve-SFT-40K by synthesizing high-quality reasoning trajectories from 331 real tables with a strong teacher model and strict filtering, and introduce TAB-GRPO, a reinforcement learning recipe that jointly optimizes evidence selection and prediction correctness with separate rewards and stabilizes mixed regression and classification training via dynamic task-advantage balancing; on a held-out benchmark of 75 classification and 52 regression tables, TabSieve consistently improves performance across shot budgets, with