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University of British Columbia, Vancouver, BC, Canada, Vector Institute, Toronto, ON, Canada
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Current vision-language models falter in ultra-resolution reasoning, with errors primarily stemming from evidence grounding and local perception.
Reward hacking isn't just about incentives, it's about wild directional swings in your model's parameter space – and constraining those swings can keep your LM on the straight and narrow.
