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MLLMs are failing to visually track events in videos, performing only modestly above baseline despite strong results on other benchmarks.
TRQAM stabilizes off-policy reinforcement learning by precisely controlling deviations from pretrained policies, leading to a 68% success rate—22% higher than the best prior method.
