{ "metadata": { "version": "1.2.0", "updated_at": "2026-10-19", "search_refreshed_at": "2026-10-19", "topic_id": "peft", "name": "Parameter-efficient building blocks", "definition": "Adapt a frozen model with a small set of learned parameters.", "record_count": 2, "topic_url": "https://programasweights.com/reading-list/guides/compilation/#peft", "source_url": "https://programasweights.com/reading-list/data/papers.json" }, "comparisons": [ { "title": "Program form and compiler", "text": "LoRA represents adaptation as low-rank weight updates; prefix tuning uses continuous activations. Their original methods learn these parameters from examples. 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Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen", "first_public": "2021-06-17", "first_public_source": "https://arxiv.org/abs/2106.09685", "arxiv_first_submission": "2021-06-17", "date_label": "arXiv v1", "arxiv": "2106.09685", "paper_url": "https://arxiv.org/abs/2106.09685", "venue": "ICLR 2022", "lens": [ "peft" ], "related_terms": [ "LoRA", "low-rank adaptation", "parameter-efficient fine-tuning", "adapters" ], "summary": "Adapts a pretrained model with low-rank weight updates while keeping the original weights fixed.", "contribution": "Reduces the parameters trained and stored for each task. 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