SHAPE is an analytical framework examining chain-of-thought reasoning in LLMs through two mathematics-education lenses: semantic spaces (different mathematical interpretations) and heuristics (specific mathematical actions). The authors analyze how models reason, evaluate whether post-training improves mathematical proficiency, and show that promoting diverse heuristics during reinforcement-learning-based post-training enhances accuracy on math tasks.
