Unverified50% confidenceFactExact time
In autoregressive generative models, the network outputs a real-valued vector (logits) covering the entire vocabulary at each step, which is passed through softmax to obtain the probability distribution of the next token, and sampling strategies such as temperature sampling, top-k, and top-p then determine the actual output.
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10/24/2026
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No Fine-Tuning, Real-Time Jam: Sampling Control for Pretrained Music Transformers
redditr/MachineLearning10/24/2026
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Unverified模型对所有Token计算原始得分Logit后，通过Softmax函数将得分归一化为概率分布，所有Token的概率之和等于175% similarUnverifiedIn a single one-step denoising step, the model directly reads out the probability distribution for each answer slot rather than sampling token by token.75% similarUnverifiedDecoding strategy refers to how a model selects output from a probability distribution when generating each token; common strategies include greedy decoding, beam search, temperature sampling, top-k sampling and nucleus sampling, among others.73% similarUnverifiedIn autoregressive language models, logits are the unnormalized scores the model gives to each token in the vocabulary, which only become probabilities after softmax.71% similarUnverifiedThe core mechanism of large model inference is the continuous prediction of the probability of the next word, converting raw scores into a probability distribution summing to 1 via the Softmax function.71% similar
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