Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models

• URL: http://arxiv.org/abs/2506.06137v1
• Date: Fri, 06 Jun 2025 14:52:19 GMT
• Title: Table-r1: Self-supervised and Reinforcement Learning for Program-based Table Reasoning in Small Language Models
• Authors: Rihui Jin, Zheyu Xin, Xing Xie, Zuoyi Li, Guilin Qi, Yongrui Chen, Xinbang Dai, Tongtong Wu, Gholamreza Haffari,
• Abstract summary: Table reasoning (TR) requires structured reasoning over semi-structured data.<n>Small language models (SLMs) have limited capacity compared to large LMs (LLMs, e.g., GPT-4o)<n>We propose program-based TR (P-TR), which circumvents key limitations of text-based TR (T-TR) by generating executable programs.<n>Experiments on four TR benchmarks demonstrate that Table-r1 outperforms all SLM-based methods.
• Score: 52.94091440130039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Table reasoning (TR) requires structured reasoning over semi-structured tabular data and remains challenging, particularly for small language models (SLMs, e.g., LLaMA-8B) due to their limited capacity compared to large LMs (LLMs, e.g., GPT-4o). To narrow this gap, we explore program-based TR (P-TR), which circumvents key limitations of text-based TR (T-TR), notably in numerical reasoning, by generating executable programs. However, applying P-TR to SLMs introduces two challenges: (i) vulnerability to heterogeneity in table layouts, and (ii) inconsistency in reasoning due to limited code generation capability. We propose Table-r1, a two-stage P-TR method designed for SLMs. Stage 1 introduces an innovative self-supervised learning task, Layout Transformation Inference, to improve tabular layout generalization from a programmatic view. Stage 2 adopts a mix-paradigm variant of Group Relative Policy Optimization, enhancing P-TR consistency while allowing dynamic fallback to T-TR when needed. Experiments on four TR benchmarks demonstrate that Table-r1 outperforms all SLM-based methods, achieving at least a 15% accuracy improvement over the base model (LLaMA-8B) across all datasets and reaching performance competitive with LLMs.

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