Touch is a key modality for embodied agents to understand the physical world. Although recent work has incorporated tactile signals into lan- guage systems for tactile Commonsense Rea- soning, scaling such systems to realistic open- world settings remains challenging due to two key bottlenecks: (1) current tactile reasoning datasets remain limited in format and scale, pro- viding insufficient supervision for reasoning from tactile observations to physical common- sense and hindering the learning of transferable tactile commonsense; (2) Tactile signals are inherently redundant and action-specific, yet existing methods often overlook these proper- ties, resulting in inefficient representations with limited semantic expressiveness. To address these limitations, we propose TouchThinker, a tactile-language framework that scales tac- tile commonsense reasoning to the open world from both data and representation perspec- tives. First, we construct TouchThinker-1M, a million-scale, multi-source tactile reasoning dataset covering 410 objects, 8 scenarios, and 7 sensor types, providing a solid data founda- tion for open-world generalization. We fur- ther introduce TouchThinker-Bench, an open- world benchmark with more realistic and di- verse tasks. Then, we further introduce action- aware tactile modeling and a two-stage training paradigm to improve tactile representation effi- ciency and enable efficient reasoning. Experi- mental results demonstrate that TouchThinker achieves competitive performance against state- of-the-art models across multiple datasets. Our code and dataset will be released.