Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective
For practitioners deploying large reasoning models on table reasoning tasks, EcoTab reduces inference cost while maintaining high accuracy by addressing a previously underexplored bottleneck in stepwise routing.
EcoTab introduces a table-aware stepwise routing framework that separately estimates uncertainties of table and text tokens to dynamically assign reasoning steps to smaller or larger models, achieving a better accuracy-efficiency trade-off on table reasoning benchmarks.
Large Reasoning Models (LRMs) achieve strong performance on table reasoning tasks but incur substantial inference cost due to long reasoning traces. Stepwise model routing mitigates this issue by dynamically assigning reasoning steps to smaller or larger models. However, stepwise model routing for table reasoning remains underexplored. Through empirical analysis, we find that reasoning steps involving tables contain two types of tokens with distinct uncertainty distributions: table tokens grounded in table structure, such as cell values and headers, and text tokens representing surrounding natural-language reasoning. The uncertainty of both token types is correlated with the risk that the model makes an error in the next reasoning step. However, existing methods fail to model them separately, leading to suboptimal routing decisions. To address this, we propose EcoTab, a table-aware stepwise routing framework for efficient table reasoning. At each reasoning step, EcoTab separately estimates the uncertainties of table tokens and text tokens, maps them to next-step failure risks for the small model, and combines the two risks for routing. Experiments on multiple table reasoning benchmarks show that EcoTab consistently outperforms strong baselines and achieves a better balance between accuracy and efficiency.