TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular
  Reasoning
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.
68e5bbf7975ac4c405ef200c
Jiaru Zou
68e5bbf7975ac4c405ef200d
Soumya Roy
claimed_verified
2025-10-08T08:00:57.655Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/1c8Jk7E-6BBPVoQ8nKe5N.png
Soumya Roy
user
roy-soumya-work
68e5bbf7975ac4c405ef200e
Vinay Kumar Verma
claimed_verified
2025-10-08T05:50:03.309Z
64c4c761958100e5bd302cfd
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Vinay Kumar Verma
user
vkvermaa
68e5bbf7975ac4c405ef200f
Ziyi Wang
68e5bbf7975ac4c405ef2010
David Wipf
68e5bbf7975ac4c405ef2011
Pan Lu
claimed_verified
2025-10-09T06:52:01.596Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/60f5f68fa7fd83d025749234/gCeJAZfzaANAcEvI6v5-P.jpeg
Pan Lu
user
lupantech
68e5bbf7975ac4c405ef2012
Sumit Negi
68e5bbf7975ac4c405ef2013
James Zou
68e5bbf7975ac4c405ef2014
Jingrui He
TaTToo: Tool-Grounded Thinking PRM for Test-Time Scaling in Tabular
  Reasoning
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.
TaTToo, a novel table-grounded Process Reward Model, enhances tabular reasoning by explicitly addressing table-specific operations and integrating tool-based verification, leading to significant performance improvements over existing PRMs.
Process Reward Models large reasoning models test-time scaling tabular reasoning sub-table retrieval schema interaction TaTToo data curation pipeline step-level annotations tool-based verification dual-stage paradigm cold-start supervised fine-tuning reinforcement learning reward shaping policy improvement numerical reasoning fact-checking data analysis generalizability