Unverified50% confidenceFactExact time
MTP (multi-token prediction) adds extra prediction heads during training to predict multiple future tokens simultaneously in a single forward pass, requiring no separate draft model, but the candidate tokens lack autoregressive dependencies among them, so the acceptance rate is usually lower than that of dedicated speculative decoding methods.
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Long-term
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10/21/2026
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UnverifiedMTP's multi-step prediction objective increases training memory overhead by approximately 15–25%, and requires careful tuning of the loss weights for each prediction head to avoid degradation of main-task performance.72% similarUnverified预训练阶段模型通过自监督学习方式（通常是下一个 token 预测任务）在海量文本语料（通常达数万亿 token 规模）上进行训练72% similarUnverifiedSelf-supervised learning does not require manual annotation; instead, it constructs supervision signals by predicting the next token from the given preceding context, which is the key to scaling up LLM training.70% similarUnverifiedRetaining MTP capability during fine-tuning is a technical challenge, because if the training pipeline does not correctly handle the extra prediction head parameters, the weights of the MTP module will be corrupted or reset.69% similarVerifiedDuring the pre-training phase, LLMs optimize token prediction probability through maximum likelihood estimation, lacking an inherent mechanism to 'know what they don't know,' and there is no necessary correlation between confidence and accuracy.68% similar
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