Feature-level explanations are commonly used to interpret transformer-based NLP models. Still, little is known about how explanation-aware objectives influence model behaviour during training. While prior work has demonstrated training-time manipulation of explanations in vision models, its implications for transformers and token-level explainability remain unexplored. We study training-time manipulation of token-level explanations in transformer-based NLP classifiers and introduce sequence-aware objectives suited to text input. We show that explanation-aware training systematically alters token relevance patterns while largely preserving task accuracy. Importantly, masking and cross-method evaluations reveal that these attribution changes can coincide with shifts in functional model reliance rather than isolated failures of specific explanation methods. Our results suggest that apparent vulnerabilities of feature-level explanations can reflect deeper model adaptations, underscoring the need to consider learning dynamics when interpreting explanation robustness.