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Context-augmented RL lets smaller MLLMs punch *way* above their weight, rivaling much larger models on reasoning tasks while dodging reward hacking.
By decoupling coarse action consistency from fine-grained variations, PF-DAG achieves state-of-the-art imitation learning performance in robotic manipulation tasks.
