Scaling Tasks, Not Samples: Mastering Humanoid Control through Multi-Task Model-Based Reinforcement Learning

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• Scaling the number of tasks rather than samples per task is more effective for online robotic learning.method 90%
• Model-based reinforcement learning has a structural advantage over model-free methods in multi-task settings because physical dynamics are invariant across tasks.technical 85%
• Model-free methods suffer from gradient interference when tasks demand conflicting actions in similar states.technical 80%
• Task diversity acts as a regularizer for model-based reinforcement learning, improving dynamics learning and sample efficiency.technical 85%
• EfficientZero-Multitask (EZ-M) achieves state-of-the-art performance on HumanoidBench with significantly higher sample efficiency than strong baselines.result 90%
• EZ-M achieves its results without extreme parameter scaling.method 85%
• Recent progress emphasizing scaling model parameters and offline datasets is limited in robotics where learning requires active interaction.limitation 80%
• Task scaling is a critical axis for scalable robotic learning.technical 85%

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