Unverified50% confidenceFactExact time
KL(P||Q) measures how many extra bits of information are needed on average when using Q to encode samples from P.
1
Sources
50%
Confidence
Long-term
Relevance
11/3/2026
First Seen
Sources
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Related Claims
UnverifiedIn speculative decoding, if the draft model's per-position accuracy is p, the expected number of tokens accepted when generating k candidate tokens is p*(1-p^k)/(1-p).71% similarUnverifiedKLD (KL divergence) measures the difference between two probability distributions, with the formula D_KL(P||Q)=∑_x P(x)log(P(x)/Q(x)).68% similarUnverifiedThe attention computation flow is: the Q vector performs dot products with the K vectors of all preceding tokens to obtain relevance scores, which are normalized via Softmax and then used to compute a weighted sum of V vectors, followed by a linear transformation for output.67% similarUnverifiedThe scheduling-averaged divergence first computes the KL divergence under each schedule and then takes the expectation, i.e., E_U D_KL(p||q_U).66% similarUnverifiedLLM推理吞吐通常以token数为单位衡量（tokens/s），而非请求数（QPS）65% similar
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