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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.
Task-oriented dialogue agents can now learn to balance user satisfaction and operational costs, thanks to a new RL framework that optimizes for both.
