BWB-KD: Domain Knowledge Distillation via Knowledge Injection, Reasoning Organization, and Activation

Transferring domain expertise from black-box teachers while preserving general capabilities remains a challenge in domain adaptation. Direct supervised adaptation can improve domain performance at the cost of broader capabilities, while subsequent reinforcement learning does not reliably resolve this trade-off. We propose BWB-KD, a stage-coordinated distillation framework that connects knowledge injection, reasoning organization, and outcome activation through complementary reasoning modes. Non-thinking supervised fine-tuning first builds a domain expert from black-box demonstrations. Thinking-mode on-policy distillation then uses the frozen expert to guide a base-initialized student along student-generated reasoning trajectories. Finally, non-thinking reinforcement learning optimizes the transferred student using outcome feedback. This off–on–off design places expert-guided reasoning organization between knowledge acquisition and task optimization. Experiments on Qwen3.6-27B across three biomedical tasks show improved domain performance over supervised fine-tuning followed by reinforcement learning, with larger gains in non-thinking inference. General-capability evaluations further show better non-thinking retention. Comparisons with offline distillation, alternative reinforcement learning methods, and different stage orders and reasoning modes support the value of coordinating how domain knowledge is acquired, transferred, and reinforced.