Unverified50% confidenceFactExact time
In the experiment, the original base model (using its own tokenizer) was fine-tuned with the same recipe as a control group; both models used the same training data and pipeline, and the only variable was whether the tokenizer had undergone programming-language-aware extension.
1
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50%
Confidence
Medium-term (~90 days)
Relevance
10/23/2026
First Seen
Valid until: 1/21/2027
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UnverifiedThe researchers trained small masked diffusion language models on the same corpora as a comparison against count-based prediction.78% similarUnverifiedThe tokenizer and the model are two separate things; its vocabulary is fixed before model training and does not change after training is complete.77% similarUnverified同样的模型只改几句提示词，回答效果可能天壤之别，提示词设计本质上是持续测试、不断调整、反复优化的循环76% similarUnverifiedDuring the training phase, the decoder can use teacher forcing to process the entire target sequence in parallel, whereas during inference the model can only generate output autoregressively one token at a time.76% similar
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