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Evolution strategies can optimize large language models for long-horizon tasks with minimal GPU resources, outperforming traditional reinforcement learning approaches.
VLMs can't play the shell game: they fail to track visually identical objects over time, but a simple fine-tuning strategy using generated trajectories can fix this.
