Training models for both fast responses and deep reasoning requires careful balancing—prioritizing one mode degrades the other, and the optimal training schedule depends on how much data you allocate to each mode.
This paper studies how to train language models that can switch between quick answers and detailed reasoning. The researchers found that training these two modes together creates a trade-off: focusing more on quick answers hurts the model's ability to reason deeply. They tested different training schedules and data ratios to find the best balance for math problems.