Leveraging Instruction Tuning and Merging for Reasoning Model Adaptation
This work proposes a two-step pipeline to adapt reasoning language models (RLMs) to new tasks without explicit reasoning traces. First, models are fine-tuned using standard instruction tuning. Second, model merging is used with a calibration set to recover lost reasoning abilities while maintaining task performance. The method efficiently preserves core logic during adaptation.