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Training & Fine-tuningHard· Training Strategy· Convergence· Resource Allocation
Research wants to train a 300M model to full convergenc...
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See plansWhile training a model to full convergence is theoretically sound for maximizing data utility, the proposed plan leaves 40% of the compute budget unspent while only utilizing 12% of the available data window. A more efficient approach is to train a larger model (2.5B) over the same four-epoch window. The deciding factor should be the nature of the evaluation gap: if the gap is in factual recall, the existing retrieval index already addresses it, making the larger model unnecessary.