A dual-phase framework that uses a Censor satisfaction scorer to expand training data and then applies SFT plus frequency-weighted DPO, reporting benchmark gains over SFT, PO, and memory baselines.
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A Novel Self-Evolution Framework for Large Language Models
A dual-phase framework that uses a Censor satisfaction scorer to expand training data and then applies SFT plus frequency-weighted DPO, reporting benchmark gains over SFT, PO, and memory baselines.