Unverified95% confidenceFactTime unknown
传统自回归推理中，模型每一步只能基于已有上下文预测下一个token，是串行依赖关系
1
Sources
95%
Confidence
Long-term
Relevance
6/1/2026
First Seen
Sources
Qwen 3.6 MTP Real-World Test: The Secret to a 20% Speed Boost with Just Three Parameters
bilibili比特光锥_BightCone
Related Entities
Related Claims
Unverified大模型推理的本质是自回归生成，即不断预测下一个token85% similarUnverified分类模型的推理是非自回归的，仅需一次前向传播即可完成所有位置预测，计算效率显著高于生成模型80% similarUnverifiedLarge language models use an autoregressive generation mechanism, sampling the next token at each step based solely on a probability distribution, with each token's generation conditioned on all preceding tokens to form a chain of probabilistic dependencies.78% similarUnverifiedEach time a token is generated via a forward pass, the model must go through a complete multi-head attention computation and feed-forward network layers, with the amount of computation determined by the model's parameter scale75% similarVerifiedChain-of-Thought（思维链）提示技术通过强制模型先输出中间步骤来部分缓解自回归模型在逻辑推理中的缺陷75% similar
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