Mechanism
Supervises interleaved natural-language deduction and executable code/tool blocks: , masking environment execution outputs from the cross-entropy loss so the model learns when to offload symbolic/numerical computation and how to interpret stdout/stderr.
Practical Notes
Masking tool observation tokens (obs_t) during SFT is mandatory so the model does not learn to hallucinate fake interpreter outputs.
References
- ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving ↗
All 8 authors
Zhibin Gou, Zhihong Shao, Yeyun Gong, Yelong Shen, Yujiu Yang, Minlie Huang, Nan Duan, Weizhu Chen
- OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset ↗