Token-level teacher selection during training outperforms fixed or response-level teacher assignment—different parts of a model's output benefit from different supervision signals.
This paper proposes MTA-OPSD, a method that assigns different teacher models to individual tokens during language model training. Unlike existing approaches that use a single fixed teacher or vary teachers only between responses, this method lets each token choose its best teacher through voting and KL divergence selection.