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A novel bandit algorithm finds the optimal self-speculative decoding configuration for VLMs in real-time, boosting inference speed by up to 11% without retraining.
Free exploration in multi-armed bandits can lead to sharp phase transitions in accumulated regret, offering significant savings compared to standard regret minimization.