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Gemma 4's unified architecture and reasoning mode enable it to outperform larger models in human-rated tasks while maintaining high efficiency.
Entropy regularization makes planning provably easy: SmoothCruiser achieves polynomial sample complexity in MDPs where standard methods fail.
TrailBlazer offers a computationally efficient Monte-Carlo planning algorithm that drastically reduces sample complexity by focusing exploration on near-optimal state trajectories within an MDP.
