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Redirecting failure into actionable corrections boosts robotic manipulation success rates by nearly 32% while using an order of magnitude fewer training samples.
Achieving near-FP16 accuracy with 4-bit quantization, TwinQuant offers a groundbreaking approach to optimizing large language model inference speed and efficiency.
