Table-R1: Region-based Reinforcement Learning for Table Understanding

ArXi:2505.12415v3 Announce Type: replace-cross Tables present unique challenges for language models due to their structured row-column interactions, necessitating specialized approaches for effective comprehension. While large language models (LLMs) have nstrated potential in table reasoning through prompting and techniques like chain-of-thought (CoT) and program-of-thought (PoT), optimizing their performance for table question answering remains underexplored. In this paper, we.