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Early denoising stages in diffusion models are up to 2.58脳 more vulnerable to hardware noise, but a novel approach can mitigate this without retraining.
Accepting selective mismatches in autoregressive decoding can boost throughput by over 15% without any additional training or model adjustments.
ReTopK achieves a 3.07x speedup in attention computation with only a 0.50% increase in perplexity, revolutionizing long-context processing efficiency.
