The Verifiability Gap in table reasoning arises when multimodal LLMs produce correct answers while grounding intermediate steps in unsupported visual evidence. Intermediate visual claims are often not exposed in a symbolically addressable form, making standard RL with verifiable rewards unable to reliably inspect whether a reasoning step is actually supported by the image. We mitigate this gap for table reasoning with Anchored Reasoning, which turns each reasoning step into a checkable (coordinate, value, structural role) claim over the table grid. We instantiate this paradigm as V-tableR1: a policy VLM generates anchor-laden visual chains-of-thought, while a learned critic VLM estimates anchor fidelity against the source image. The policy is trained with PGPO, a GRPO-based process-guided formulation that integrates critic-gated trajectory rewards with stabilization mechanisms for long anchor-laden rollouts. V-tableR1 (4B) achieves the best open-source average accuracy across seven tabular reasoning benchmarks, obtains state-of-the-art results on several individual datasets, surpasses open-source models up to 18× its size in aggregate, and improves over its SFT baseline by up to 10.66% absolute accuracy.