Training Specialist Models without Reasoning Trajectories for Domain Expert Distillation
Specialist distillation effectively transfers domain expertise to student models via teacher-generated reasoning trajectories. However, when these specialists are trained solely on question--answer pairs without explicit reasoning supervision, what governs the trajectories they generate? In this work, we show that specialist optimization implicitly selects from this latent traj
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Specialist distillation effectively transfers domain expertise to student models via teacher-generated reasoning trajectories. However, when these specialists are trained solely on question--answer pairs without explicit reasoning supervision, what governs the trajectories they generate? In this work, we show that specialist optimization implicitly selects from this latent traj
This research explores how specialist models implicitly learn and generate reasoning trajectories even without explicit supervision, which is crucial for developing more autonomous and capable AI agents. Understanding this mechanism can lead to more efficient and effective domain expert distillation, reducing the need for costly human-annotated reasoning data.