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Achieving a 90% boost in prefill throughput for MoE models could redefine the efficiency of large-scale language model serving.
VLMs can be significantly boosted on embodied tasks by mid-training on a carefully curated subset of VLM data that is highly aligned with the VLA domain, rivaling the performance of much larger models.
