Conflict-Aware Model Merging for Data-Free Long-to-Short Reasoning

Chain-of-thought (CoT) reasoning improves performance on challenging problems by generating structured reasoning steps, but it often produces unnecessarily long responses to easy ones. While typical long-to-short methods require additional training, calibration data, or input-dependent inference, model merging provides a promising way by combining long-CoT model with short model in parameter space. However, existing merging approaches either apply uniform coefficients across layers or rely on auxiliary data or activation, lacking systematic analysis about conflicts between long-CoT and short models. Importantly, strong CoT update is not necessarily beneficial when it conflicts with corresponding short-model update, motivating an explicit conflict-aware allocation of reasoning strength across layers. In this paper, we identify two patterns that guide long-to-short merging: (1) strength of CoT update, which reflects the potential reasoning benefit, and (2) conflict with short model, which indicates the risk of disrupting short-answer behavior at certain layer. Based on this observation, we propose BCL-Merge, a data-free method that design corresponding signals to select layers and assign proper coefficients into short model under given reasoning strength budget. Under explicit assumptions, we show that the resulting layer selection and strength allocation optimize an entropy-regularized proxy objective that balances reasoning benefit against short-answer disruption. Extensive experiments across reasoning benchmarks show that our method reaches 46.3% accuracy on Qwen2.5-1.5B, outperforming existing merging method by 2.9% while reducing response length by 81.5%. Notably, it also exceeds performance of long-CoT model by 5.3%. Further analysis reveals that our method shortens responses on easy examples and improves accuracy on hard examples, validating its utility.