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STARE not only prevents policy entropy collapse but also enhances accuracy by up to 8% across diverse tasks, showcasing a new frontier in stable RL training.
You can achieve near-optimal regret in multi-armed bandits with heterogeneous noise, even without knowing the best data source *a priori*, by adaptively pruning noisy sources.