TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular
  Reasoning
Authors: name: Jiaru Zou, name: Soumya Roy, name: Vinay Kumar Verma, name: Ziyi Wang, name: David Wipf, name: Pan Lu, name: Sumit Negi, name: James Zou, name: Jingrui He
Organization: Amazon

Process Reward Models (PRMs) have recently emerged as a powerful framework
for enhancing the reasoning capabilities of large reasoning models (LRMs),
particularly in the context of test-time scaling (TTS). However, their
potential for supervising LRMs on tabular reasoning domains remains
underexplored. Through detailed empirical analyses, we identify that existing
PRMs, though widely adopted for supervising text-only reasoning steps, struggle
with table-specific operations such as sub-table retrieval and schema
interaction, leading to critical performance bottlenecks. To address this
limitation, we propose TaTToo, a novel table-grounded PRM framework that (i)
reasons explicitly over tabular reasoning steps and (ii) integrates tool-based
verification to provide precise reward supervision. Concretely, we first design
a scalable data curation pipeline that constructs over 60k high-quality
step-level annotations by integrating table verification rationales with
tool-based executions. Building on the collected data, we train TaTToo with a
dual-stage paradigm: cold-start supervised fine-tuning to capture tool-use
reasoning patterns, followed by reinforcement learning with tool-grounded
reward shaping to align our model with table-based verification. We provide a
comprehensive evaluation of the policy improvement induced by our newly
designed PRM. Across 5 challenging tabular reasoning benchmarks covering
numerical reasoning, fact-checking, and data analysis, TaTToo improves
downstream policy LRMs by 30.9% at inference, surpasses strong PRM baselines
such as Qwen-2.5-Math-PRM-72B with only 8B parameters, and demonstrates strong
generalizability across diverse TTS strategies.