{
  "metadata": {
    "version": "1.3.0",
    "updated_at": "2026-11-14",
    "search_refreshed_at": "2026-10-19",
    "topic_id": "neural-programs",
    "name": "Programs and reusable artifacts",
    "definition": "Learn programs that can be saved and run on new inputs.",
    "record_count": 4,
    "topic_url": "https://programasweights.com/reading-list/guides/fuzzy-functions/",
    "source_url": "https://programasweights.com/reading-list/data/papers.json"
  },
  "comparisons": [
    {
      "title": "Code and neural programs",
      "text": "ALCHEmist generates symbolic labeling programs. PAW compiles fuzzy functions into text and learned parameters. Both build an artifact once and reuse it; one runs as code, the other on a neural interpreter.",
      "source_ids": [
        "2407.11004",
        "2607.02512"
      ],
      "source_urls": [
        "https://proceedings.neurips.cc/paper_files/paper/2024/hash/72802bef5cf1a3449e909b20c2ae18d5-Abstract-Conference.html",
        "https://arxiv.org/html/2607.02512v1#S6"
      ],
      "source_labels": [
        "ALCHEmist paper",
        "PAW evaluation"
      ]
    }
  ],
  "demos": [

  ],
  "papers": [
    {
      "id": "1511.06279",
      "title": "Neural Programmer-Interpreters",
      "authors": "Scott Reed, Nando de Freitas",
      "first_public": "2015-11-19",
      "first_public_source": "https://arxiv.org/abs/1511.06279",
      "arxiv_first_submission": "2015-11-19",
      "date_label": "arXiv v1",
      "arxiv": "1511.06279",
      "paper_url": "https://arxiv.org/abs/1511.06279",
      "venue": "ICLR 2016",
      "lens": [
        "neural-programs"
      ],
      "related_terms": [
        "neural programmer-interpreter",
        "neural programs",
        "program memory",
        "compositional execution"
      ],
      "summary": "Learns neural programs that share a recurrent interpreter and a program memory.",
      "contribution": "Learns from execution traces and composes subprograms for addition, sorting, and 3D-object canonicalization.",
      "limitations": "Training requires execution traces and task-specific interfaces.",
      "program_form": "Program embeddings stored in a key-value memory.",
      "conditioning": "A program identifier, arguments, and observations of the environment.",
      "execution": "A recurrent interpreter calls subprograms and takes actions in the environment.",
      "relationship": "PAW cites this work on learned program representations and shared interpreters.",
      "relationship_source": "https://arxiv.org/html/2607.02512v1#S10",
      "provenance": "arXiv record and paper.",
      "author_names": [
        "Scott Reed",
        "Nando de Freitas"
      ],
      "record_url": "https://programasweights.com/reading-list/papers/1511.06279/",
      "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-1511-06279",
      "citation_date": "2015-11-19",
      "json_url": "https://programasweights.com/reading-list/papers/1511.06279/record.json",
      "markdown_url": "https://programasweights.com/reading-list/papers/1511.06279/index.md",
      "bibtex_url": "https://programasweights.com/reading-list/papers/1511.06279/citation.bib",
      "csl_url": "https://programasweights.com/reading-list/papers/1511.06279/citation.csl.json",
      "publication": {
        "title": "Neural Programmer-Interpreters",
        "author_names": [
          "Scott Reed",
          "Nando de Freitas"
        ],
        "year": 2016,
        "type": "paper-conference",
        "container_title": "International Conference on Learning Representations",
        "url": "https://arxiv.org/abs/1511.06279",
        "source_url": "https://arxiv.org/pdf/1511.06279",
        "verified_at": "2026-10-19",
        "source_note": "The paper PDF identifies ICLR 2016; the conference program confirms the presentation."
      },
      "publication_bibtex_url": "https://programasweights.com/reading-list/papers/1511.06279/publication.bib",
      "publication_csl_url": "https://programasweights.com/reading-list/papers/1511.06279/publication.csl.json",
      "summary_zh": "学习由共享循环解释器执行的神经程序，并把程序表示存入程序记忆。",
      "translations": {
        "zh-CN": {
          "summary": "学习由共享循环解释器执行的神经程序，并把程序表示存入程序记忆。",
          "relationship": "PAW 引用这项关于学习程序表示和共享解释器的研究，并将编译器与解释器接口用于自然语言定义的模糊函数。",
          "program_form": "存在键值记忆中的程序嵌入。",
          "conditioning": "程序标识、参数和环境观测。",
          "execution": "循环解释器调用子程序，并在环境中执行动作。"
        }
      }
    },
    {
      "id": "2407.11004",
      "title": "The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators",
      "authors": "Tzu-Heng Huang, Catherine Cao, Vaishnavi Bhargava, Frederic Sala",
      "first_public": "2024-06-25",
      "first_public_source": "https://arxiv.org/abs/2407.11004",
      "arxiv_first_submission": "2024-06-25",
      "date_label": "arXiv v1",
      "arxiv": "2407.11004",
      "paper_url": "https://arxiv.org/abs/2407.11004",
      "venue": "NeurIPS 2024",
      "lens": [
        "neural-programs"
      ],
      "related_terms": [
        "ALCHEmist",
        "program synthesis",
        "automated labeling",
        "reusable tools"
      ],
      "summary": "Asks a language model to write labeling programs that can be saved and run locally.",
      "contribution": "Reuses generated code across many labeling decisions and combines noisy program outputs with weak supervision.",
      "limitations": "Label quality depends on the model’s ability to write useful programs. Complex tasks can produce code that is harder to inspect.",
      "program_form": "Symbolic labeling programs.",
      "conditioning": "A task description, labeling instructions, and a function signature.",
      "execution": "Generated programs label examples locally; their outputs can be combined to train a downstream model.",
      "relationship": "PAW evaluates an ALCHEmist-style approach. Both build reusable tools: ALCHEmist produces symbolic code, while PAW produces programs for a neural interpreter.",
      "relationship_source": "https://arxiv.org/html/2607.02512v1#S10",
      "provenance": "arXiv record and paper.",
      "author_names": [
        "Tzu-Heng Huang",
        "Catherine Cao",
        "Vaishnavi Bhargava",
        "Frederic Sala"
      ],
      "record_url": "https://programasweights.com/reading-list/papers/2407.11004/",
      "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2407-11004",
      "citation_date": "2024-06-25",
      "json_url": "https://programasweights.com/reading-list/papers/2407.11004/record.json",
      "markdown_url": "https://programasweights.com/reading-list/papers/2407.11004/index.md",
      "bibtex_url": "https://programasweights.com/reading-list/papers/2407.11004/citation.bib",
      "csl_url": "https://programasweights.com/reading-list/papers/2407.11004/citation.csl.json",
      "publication": {
        "title": "The ALCHEmist: Automated Labeling 500x CHEaper than LLM Data Annotators",
        "author_names": [
          "Tzu-Heng Huang",
          "Catherine Cao",
          "Vaishnavi Bhargava",
          "Frederic Sala"
        ],
        "year": 2024,
        "type": "paper-conference",
        "container_title": "Advances in Neural Information Processing Systems",
        "url": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/72802bef5cf1a3449e909b20c2ae18d5-Abstract-Conference.html",
        "source_url": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/72802bef5cf1a3449e909b20c2ae18d5-Abstract-Conference.html",
        "verified_at": "2026-10-19",
        "volume": "37",
        "doi": "10.52202/079017-2003",
        "pages": "62648-62672"
      },
      "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2407.11004/publication.bib",
      "publication_csl_url": "https://programasweights.com/reading-list/papers/2407.11004/publication.csl.json",
      "method_comparisons": [
        {
          "title": "Code and neural programs",
          "text": "ALCHEmist generates symbolic labeling programs. PAW compiles fuzzy functions into text and learned parameters. Both build an artifact once and reuse it; one runs as code, the other on a neural interpreter.",
          "source_ids": [
            "2407.11004",
            "2607.02512"
          ],
          "source_urls": [
            "https://proceedings.neurips.cc/paper_files/paper/2024/hash/72802bef5cf1a3449e909b20c2ae18d5-Abstract-Conference.html",
            "https://arxiv.org/html/2607.02512v1#S6"
          ],
          "source_labels": [
            "ALCHEmist paper",
            "PAW evaluation"
          ],
          "topic_id": "neural-programs",
          "topic_label": "Programs and reusable artifacts",
          "topic_url": "https://programasweights.com/reading-list/guides/fuzzy-functions/"
        }
      ],
      "summary_zh": "让语言模型编写可保存、可本地执行的标注程序，并用弱监督组合多个程序的输出。",
      "translations": {
        "zh-CN": {
          "summary": "让语言模型编写可保存、可本地执行的标注程序，并用弱监督组合多个程序的输出。",
          "relationship": "PAW 评测了 ALCHEmist 风格的方法。ALCHEmist 生成符号代码，PAW 生成由神经解释器执行的程序，两者都构建可复用的工具。",
          "program_form": "符号标注程序。",
          "conditioning": "任务描述、标注要求和函数签名。",
          "execution": "生成的程序在本地标注数据，其输出可进一步用于训练下游模型。"
        }
      }
    },
    {
      "id": "2607.02512",
      "title": "Program-as-Weights: A Programming Paradigm for Fuzzy Functions",
      "authors": "Wentao Zhang, Liliana Hotsko, Woojeong Kim, Pengyu Nie, Stuart Shieber, Yuntian Deng",
      "first_public": "2026-07-02",
      "first_public_source": "https://arxiv.org/abs/2607.02512",
      "arxiv_first_submission": "2026-07-02",
      "date_label": "arXiv v1",
      "arxiv": "2607.02512",
      "paper_url": "https://arxiv.org/abs/2607.02512",
      "venue": "NeurIPS 2026",
      "lens": [
        "neural-programs",
        "generated-adapters"
      ],
      "related_terms": [
        "Program-as-Weights",
        "ProgramAsWeights",
        "PAW",
        "fuzzy-function programming",
        "neural compiler",
        "neural interpreter",
        "FuzzyBench"
      ],
      "summary": "Proposes fuzzy-function programming: compile a natural-language function specification into a neural program, then reuse it locally.",
      "contribution": "Develops the compiler–interpreter abstraction and programs that combine text with learned parameters. Introduces the 10M-example FuzzyBench dataset, local execution interfaces, and image-conditioned compilation. Prefix tuning and LoRA are two implementations.",
      "program_form": "A discrete pseudo-program and a compiler-generated parameter-efficient module.",
      "conditioning": "A function specification, optionally with examples. The image extension also accepts visual information.",
      "execution": "A frozen lightweight interpreter loads the program and runs new inputs locally.",
      "relationship": "Compile by Training builds on this programming abstraction. It keeps the same interface and refines the fast compiler’s prediction using synthesized examples.",
      "relationship_source": "https://arxiv.org/html/2609.04199v1#S1",
      "project_url": "https://programasweights.com/",
      "code_url": "https://github.com/programasweights",
      "equal_contribution": [
        "Wentao Zhang",
        "Liliana Hotsko",
        "Woojeong Kim"
      ],
      "venue_source": "https://arxiv.org/abs/2607.02512",
      "provenance": "July 2026 arXiv paper; NeurIPS 2026.",
      "author_names": [
        "Wentao Zhang",
        "Liliana Hotsko",
        "Woojeong Kim",
        "Pengyu Nie",
        "Stuart Shieber",
        "Yuntian Deng"
      ],
      "record_url": "https://programasweights.com/reading-list/papers/2607.02512/",
      "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2607-02512",
      "citation_date": "2026",
      "json_url": "https://programasweights.com/reading-list/papers/2607.02512/record.json",
      "markdown_url": "https://programasweights.com/reading-list/papers/2607.02512/index.md",
      "bibtex_url": "https://programasweights.com/reading-list/papers/2607.02512/citation.bib",
      "csl_url": "https://programasweights.com/reading-list/papers/2607.02512/citation.csl.json",
      "publication": {
        "title": "Program-as-Weights: A Programming Paradigm for Fuzzy Functions",
        "author_names": [
          "Wentao Zhang",
          "Liliana Hotsko",
          "Woojeong Kim",
          "Pengyu Nie",
          "Stuart Shieber",
          "Yuntian Deng"
        ],
        "year": 2026,
        "type": "paper-conference",
        "container_title": "The Fortieth Annual Conference on Neural Information Processing Systems",
        "url": "https://arxiv.org/abs/2607.02512",
        "source_url": "https://arxiv.org/abs/2607.02512",
        "verified_at": "2026-10-24",
        "preferred_citation": true,
        "citation_key": "zhang2026programasweights"
      },
      "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2607.02512/publication.bib",
      "publication_csl_url": "https://programasweights.com/reading-list/papers/2607.02512/publication.csl.json",
      "preferred_citation": {
        "title": "Program-as-Weights: A Programming Paradigm for Fuzzy Functions",
        "author_names": [
          "Wentao Zhang",
          "Liliana Hotsko",
          "Woojeong Kim",
          "Pengyu Nie",
          "Stuart Shieber",
          "Yuntian Deng"
        ],
        "year": 2026,
        "type": "paper-conference",
        "container_title": "The Fortieth Annual Conference on Neural Information Processing Systems",
        "url": "https://arxiv.org/abs/2607.02512",
        "source_url": "https://arxiv.org/abs/2607.02512",
        "verified_at": "2026-10-24",
        "preferred_citation": true,
        "citation_key": "zhang2026programasweights"
      },
      "citation_url": "https://arxiv.org/abs/2607.02512",
      "preprint_citation_date": "2026-07-02",
      "preprint_bibtex_url": "https://programasweights.com/reading-list/papers/2607.02512/preprint.bib",
      "preprint_csl_url": "https://programasweights.com/reading-list/papers/2607.02512/preprint.csl.json",
      "method_comparisons": [
        {
          "title": "From generated adapters to reusable functions",
          "text": "Hypter, HINT, HyperTuning, and Text-to-LoRA generate adaptations from descriptions or examples. PAW uses generated parameters as part of a program that can be saved, called from ordinary code, and reused by a fixed interpreter.",
          "source_ids": [
            "2101.00420",
            "2212.10315",
            "2211.12485",
            "2506.06105",
            "2607.02512"
          ],
          "source_urls": [
            "https://arxiv.org/html/2607.02512v1#S10",
            "https://proceedings.mlr.press/v267/charakorn25a.html"
          ],
          "source_labels": [
            "PAW related work",
            "Text-to-LoRA paper"
          ],
          "topic_id": "generated-adapters",
          "topic_label": "Generated weights and adapters",
          "topic_url": "https://programasweights.com/reading-list/guides/compilation/#generated-adapters"
        },
        {
          "title": "Code and neural programs",
          "text": "ALCHEmist generates symbolic labeling programs. PAW compiles fuzzy functions into text and learned parameters. Both build an artifact once and reuse it; one runs as code, the other on a neural interpreter.",
          "source_ids": [
            "2407.11004",
            "2607.02512"
          ],
          "source_urls": [
            "https://proceedings.neurips.cc/paper_files/paper/2024/hash/72802bef5cf1a3449e909b20c2ae18d5-Abstract-Conference.html",
            "https://arxiv.org/html/2607.02512v1#S6"
          ],
          "source_labels": [
            "ALCHEmist paper",
            "PAW evaluation"
          ],
          "topic_id": "neural-programs",
          "topic_label": "Programs and reusable artifacts",
          "topic_url": "https://programasweights.com/reading-list/guides/fuzzy-functions/"
        },
        {
          "title": "What gets reused?",
          "text": "Gisting caches a few instruction activations. Generative Adapter stores context in parameter updates. PAW saves a function as a program containing text and a neural module.",
          "source_ids": [
            "2304.08467",
            "2411.05877",
            "2607.02512"
          ],
          "source_urls": [
            "https://arxiv.org/abs/2304.08467",
            "https://arxiv.org/abs/2411.05877",
            "https://arxiv.org/html/2607.02512v1#S2"
          ],
          "source_labels": [
            "Gist paper",
            "Generative Adapter paper",
            "PAW program definition"
          ],
          "topic_id": "context-compression",
          "topic_label": "Context compression",
          "topic_url": "https://programasweights.com/reading-list/guides/compilation/#context-compression"
        },
        {
          "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. PAW generates both forms from specifications.",
          "source_ids": [
            "2106.09685",
            "2101.00190",
            "2607.02512"
          ],
          "source_urls": [
            "https://arxiv.org/abs/2106.09685",
            "https://aclanthology.org/2021.acl-long.353/",
            "https://arxiv.org/html/2607.02512v1#S3"
          ],
          "source_labels": [
            "LoRA paper",
            "Prefix-tuning paper",
            "PAW implementations"
          ],
          "topic_id": "peft",
          "topic_label": "Parameter-efficient building blocks",
          "topic_url": "https://programasweights.com/reading-list/guides/compilation/#peft"
        },
        {
          "title": "Fast prediction and training",
          "text": "PAW’s fast compiler predicts a program. Compile by Training starts from that prediction, generates examples, and trains an adapter. Both use the same runtime interface; the extra training improves semantic accuracy on FuzzyBench-Hard at a higher compilation cost.",
          "source_ids": [
            "2607.02512",
            "2609.04199"
          ],
          "source_urls": [
            "https://arxiv.org/html/2607.02512v1#S3",
            "https://arxiv.org/html/2609.04199v1#S3",
            "https://arxiv.org/html/2609.04199v1#S5"
          ],
          "source_labels": [
            "PAW compiler implementations",
            "Compile by Training method",
            "Compile by Training evaluation"
          ],
          "topic_id": "training-compilation",
          "topic_label": "Compilation through training",
          "topic_url": "https://programasweights.com/reading-list/guides/compilation/#training-compilation"
        }
      ],
      "summary_zh": "提出模糊函数编程：将自然语言函数说明编译成神经程序，保存后由小模型在本地反复执行。",
      "translations": {
        "zh-CN": {
          "summary": "提出模糊函数编程：将自然语言函数说明编译成神经程序，保存后由小模型在本地反复执行。",
          "relationship": "PAW 建立编译器与解释器接口，程序同时包含文本和生成的参数，并提供 FuzzyBench、本地运行接口及图像条件编译。Compile by Training 沿用这一程序格式和运行时。",
          "program_form": "文本伪程序与编译器生成的参数高效模块，如 LoRA 适配器或前缀向量。",
          "conditioning": "函数说明，也支持输入输出示例；图像扩展还接收视觉信息。",
          "execution": "冻结的轻量解释器加载保存的程序，在本地处理新输入。"
        }
      }
    },
    {
      "id": "2609.04199",
      "title": "Compile by Training: Turning Natural-Language Specifications into Local Neural Functions",
      "authors": "Yuntian Deng, Pengyu Nie, Stuart Shieber",
      "first_public": "2026-09-03",
      "first_public_source": "https://arxiv.org/abs/2609.04199",
      "arxiv_first_submission": "2026-09-03",
      "date_label": "arXiv v1",
      "arxiv": "2609.04199",
      "paper_url": "https://arxiv.org/abs/2609.04199",
      "original_url": "https://arxiv.org/html/2609.04199v1",
      "venue": "EMNLP 2026 System Demonstrations",
      "venue_source": "https://aclanthology.org/2026.emnlp-demos.78/",
      "lens": [
        "neural-programs",
        "training-compilation"
      ],
      "related_terms": [
        "compile by training",
        "training-based compilation",
        "specification-specific optimization",
        "synthetic supervision",
        "local neural function",
        "FuzzyBench-Hard",
        "ProgramAsWeights"
      ],
      "summary": "Compiles a function description into a reusable local program by generating training examples and optimizing an adapter.",
      "contribution": "Makes task-specific training a software build step. Raises FuzzyBench-Hard semantic accuracy (LEM) from 22.4% to 83.6% and demonstrates composition in a website helper, avatar control, and English–Claudish translation.",
      "program_form": "A LoRA adapter, runtime prompt, specification, and interpreter metadata in a PAW program.",
      "conditioning": "The specification guides teacher synthesis. PAW’s fast compiler supplies the initial adapter and runtime prompt.",
      "execution": "A frozen Qwen3-0.6B interpreter runs the saved function locally without further teacher calls.",
      "relationship": "Extends PAW with training at compile time while keeping its program format and runtime interface.",
      "relationship_source": "https://arxiv.org/html/2609.04199v1#S1",
      "project_url": "https://programasweights.com/playground?compiler=paw-ft-bs48",
      "demo_url": "https://programasweights.com/playground?compiler=paw-ft-bs48",
      "code_url": "https://github.com/programasweights/compile-by-training",
      "provenance": "September 2026 arXiv paper; published in EMNLP 2026 System Demonstrations, pages 830–839.",
      "first_public_note": "The paper reports public-service measurements from May 2026.",
      "author_names": [
        "Yuntian Deng",
        "Pengyu Nie",
        "Stuart Shieber"
      ],
      "record_url": "https://programasweights.com/reading-list/papers/2609.04199/",
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      "citation_date": "2026",
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      "csl_url": "https://programasweights.com/reading-list/papers/2609.04199/citation.csl.json",
      "publication": {
        "title": "Compile by Training: Turning Natural-Language Specifications into Local Neural Functions",
        "author_names": [
          "Yuntian Deng",
          "Pengyu Nie",
          "Stuart Shieber"
        ],
        "year": 2026,
        "type": "paper-conference",
        "container_title": "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
        "editor_names": [
          "Sweta Agrawal",
          "Alberto Abad"
        ],
        "month": 10,
        "month_name": "October",
        "address": "Budapest, Hungary",
        "publisher": "Association for Computational Linguistics",
        "pages": "830-839",
        "bibtex_pages": "830--839",
        "doi": "10.18653/2026.emnlp-demos.78",
        "isbn": "979-8-89176-461-3",
        "url": "https://aclanthology.org/2026.emnlp-demos.78/",
        "source_url": "https://aclanthology.org/2026.emnlp-demos.78/",
        "verified_at": "2026-11-06",
        "preferred_citation": true,
        "citation_key": "deng-etal-2026-compile"
      },
      "publication_bibtex_url": "https://programasweights.com/reading-list/papers/2609.04199/publication.bib",
      "publication_csl_url": "https://programasweights.com/reading-list/papers/2609.04199/publication.csl.json",
      "preferred_citation": {
        "title": "Compile by Training: Turning Natural-Language Specifications into Local Neural Functions",
        "author_names": [
          "Yuntian Deng",
          "Pengyu Nie",
          "Stuart Shieber"
        ],
        "year": 2026,
        "type": "paper-conference",
        "container_title": "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
        "editor_names": [
          "Sweta Agrawal",
          "Alberto Abad"
        ],
        "month": 10,
        "month_name": "October",
        "address": "Budapest, Hungary",
        "publisher": "Association for Computational Linguistics",
        "pages": "830-839",
        "bibtex_pages": "830--839",
        "doi": "10.18653/2026.emnlp-demos.78",
        "isbn": "979-8-89176-461-3",
        "url": "https://aclanthology.org/2026.emnlp-demos.78/",
        "source_url": "https://aclanthology.org/2026.emnlp-demos.78/",
        "verified_at": "2026-11-06",
        "preferred_citation": true,
        "citation_key": "deng-etal-2026-compile"
      },
      "citation_url": "https://aclanthology.org/2026.emnlp-demos.78/",
      "preprint_citation_date": "2026-09-03",
      "preprint_bibtex_url": "https://programasweights.com/reading-list/papers/2609.04199/preprint.bib",
      "preprint_csl_url": "https://programasweights.com/reading-list/papers/2609.04199/preprint.csl.json",
      "method_comparisons": [
        {
          "title": "Fast prediction and training",
          "text": "PAW’s fast compiler predicts a program. Compile by Training starts from that prediction, generates examples, and trains an adapter. Both use the same runtime interface; the extra training improves semantic accuracy on FuzzyBench-Hard at a higher compilation cost.",
          "source_ids": [
            "2607.02512",
            "2609.04199"
          ],
          "source_urls": [
            "https://arxiv.org/html/2607.02512v1#S3",
            "https://arxiv.org/html/2609.04199v1#S3",
            "https://arxiv.org/html/2609.04199v1#S5"
          ],
          "source_labels": [
            "PAW compiler implementations",
            "Compile by Training method",
            "Compile by Training evaluation"
          ],
          "topic_id": "training-compilation",
          "topic_label": "Compilation through training",
          "topic_url": "https://programasweights.com/reading-list/guides/compilation/#training-compilation"
        }
      ],
      "summary_zh": "根据函数说明生成训练样本，再训练 LoRA 适配器，将一次函数定义构建成可复用的本地神经程序。",
      "translations": {
        "zh-CN": {
          "summary": "根据函数说明生成训练样本，再训练 LoRA 适配器，将一次函数定义构建成可复用的本地神经程序。",
          "relationship": "在 PAW 的快速编译结果上增加针对当前函数的训练，保留 PAW 的程序格式和解释器接口。",
          "program_form": "包含 LoRA 适配器、运行提示词、函数说明和解释器元数据的 PAW 程序。",
          "conditioning": "函数说明指导教师模型生成样本，PAW 快速编译器提供初始适配器与运行提示词。",
          "execution": "冻结的 Qwen3-0.6B 解释器执行保存的函数，不再调用教师模型。"
        }
      }
    }
  ]
}