Empirical study on five LLMs finds pretrained-to-aligned paths yield bigger gains over baseline than finetuned-to-aligned paths, though absolute accuracy remains lower for pretrained starts.
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Finetuning Phi Silica on curated short presentation text improves semantic fidelity, reduces hallucinations, and raises preference win rates over GPT-5-chat rewrites.
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Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation
Empirical study on five LLMs finds pretrained-to-aligned paths yield bigger gains over baseline than finetuned-to-aligned paths, though absolute accuracy remains lower for pretrained starts.
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Short-form Text Rewriting with Phi Silica
Finetuning Phi Silica on curated short presentation text improves semantic fidelity, reduces hallucinations, and raises preference win rates over GPT-5-chat rewrites.