{
  "metadata": {
    "version": "1.2.0",
    "updated_at": "2026-10-19",
    "search_refreshed_at": "2026-10-19",
    "topic_id": "training-compilation",
    "name": "Compilation through training",
    "definition": "Generate examples from a function specification and train a reusable neural program.",
    "record_count": 1,
    "topic_url": "https://programasweights.com/reading-list/guides/compilation/#training-compilation",
    "source_url": "https://programasweights.com/reading-list/data/papers.json"
  },
  "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"
      ]
    }
  ],
  "demos": [

  ],
  "papers": [
    {
      "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://arxiv.org/abs/2609.04199",
      "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.",
      "limitations": "Synthetic examples can inherit teacher errors. Cold compilation takes 50.9 seconds for one specification on a B300. FuzzyBench-Hard selects fast-compiler failures under exact match; the reported accuracy uses an LLM judge.",
      "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; its record lists EMNLP 2026 System Demonstrations.",
      "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/",
      "legacy_record_url": "https://programasweights.com/reading-list/papers/#paper-2609-04199",
      "citation_date": "2026-09-03",
      "json_url": "https://programasweights.com/reading-list/papers/2609.04199/record.json",
      "markdown_url": "https://programasweights.com/reading-list/papers/2609.04199/index.md",
      "bibtex_url": "https://programasweights.com/reading-list/papers/2609.04199/citation.bib",
      "csl_url": "https://programasweights.com/reading-list/papers/2609.04199/citation.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"
        }
      ]
    }
  ]
}