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AffordanceVLA transforms robotic manipulation by using structured affordance cues to create precise perception-action mappings, outperforming traditional models.
Streaming reasoning steps can boost multi-agent system performance by 7.3 percentage points on average, revealing a new dimension for scaling effectiveness and efficiency.
Standard RL rollouts can effectively provide world modeling supervision, leading to significant performance gains in language agents.
