A development team is building a language model to solve complex multi-step problems. They are debating how to best use their limited computational budget during the model's operational phase. One strategy is to use a complex 'brute-force' search at runtime, where the model generates a very large number of potential reasoning paths and then tries to find the correct one. An alternative strategy is to first use a portion of the budget for additional specialized training to improve the model's core problem-solving skills, and then use a more streamlined search process at runtime. Analyze the second strategy. Explain the specific ways in which improving the model's inherent reasoning abilities through training could make a simpler runtime search process both more efficient and more effective.