Unverified50% confidenceBenchmarkExact time
On the Thinking models, CopyLoop achieved decoding speedups of 2.74x to 3.52x compared with full-depth autoregressive decoding.
1
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50%
Confidence
Long-term
Relevance
10/30/2026
First Seen
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Related Claims
VerifiedSpeculative decoding typically delivers a 2–3× speedup in practical applications.77% similarVerifiedWhen compression techniques such as quantization, knowledge distillation, and operator fusion are used in combination, inference speed typically improves by 2–4×.75% similarUnverifiedThe upper speedup limit of the decoding stage (approximately 2.7×) is higher than the upper speedup limit of the encoding stage.75% similarVerifiedModel compression techniques such as INT8 quantization and channel pruning can increase inference speed by 2–4 times with a precision loss of less than 1% mAP72% similarUnverified潜空间推理理论上可大幅压缩所需输出token数量，因为连续向量的信息密度远高于离散文本token，有望实现数倍的效率提升72% similar
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