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Despite strong downstream performance, SLMs struggle with instruction-following due to weak alignment between speech and text representations, revealing a critical gap in their training methodology.
Retaining seemingly redundant tokens can actually enhance model performance, defying traditional assumptions about token importance in visual processing.
