Critical Thinking: Which Kinds of Complexity Govern Optimal Reasoning Length?

A framework using deterministic finite automata optimizes reasoning for task solutions, focusing on token efficiency and run length impact. Predicting optimal tokens enhances accuracy for new problems, linking DFA properties to language model performance. Dyck language validation study reveals insights into reasoning complexity and task difficulty, distinct from previous mathematical task-focused works. Models' performance across tasks, emphasizing critical lengths and generation accuracy, shows that longer generation lengths generally improve accuracy.