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LLMs stubbornly stick to task-appropriate reasoning even when explicitly instructed to use conflicting logic, but targeted interventions can nudge them towards better instruction following.
Output diversity in post-trained models collapses due to training data composition, not just post-training methods, challenging assumptions about inference-time fixes.
VLMs are more easily swayed by misleading text than you think, and their impressive reasoning chains can mask, rather than reveal, this over-reliance on language.
