Unverified50% confidenceFactExact time
The research team trained a series of decoder-only architecture models from scratch, with parameter scales ranging from 47 million to 349 million.
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50%
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Long-term
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10/9/2026
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UnverifiedTraining a language model from scratch under VRAM-constrained conditions typically involves choosing architectures with a smaller parameter count, usually on the order of 100 million to 500 million parameters.74% similarUnverifiedThe 29-million-parameter encoder trained by the researchers achieved a popularity-weighted cross-fold AUC of only 0.486 on 985 statements, indistinguishable from the corpus prior.70% similarUnverifiedEach model was paired with four prompt types—zero-shot, few-shot, chain-of-thought, and few-shot plus chain-of-thought—each in two groups, producing approximately 52,000 labels in total.67% similarUnverifiedTaking a 1000×1000 weight matrix as an example, if the LoRA rank is set to 8, only approximately 16,000 parameters need to be trained instead of the original 1,000,000 parameters.66% similarUnverifiedBefore the era of large models, the largest model the presenter had trained had only approximately 4,000 parameters.65% similar
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