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Le", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "Symbolic Discovery of Optimization Algorithms", "url": "https://arxiv.org/abs/2302.06675v4" } } ], "evidence": [ { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-0", "kind": "source_reference", "source": { "citation": "Symbolic Discovery of Optimization Algorithms", "url": "https://arxiv.org/abs/2302.06675v4" } }, { "description": "Bibliographic/stated-result source; not a WRS proof verification", "id": "source-1", "kind": "source_reference", "source": { "citation": "Chen et al.: symbolic optimizer search and Lion — author/proceedings record", "url": "https://github.com/google/automl/tree/master/lion" } } ], "origin": "AGENT_NATIVE", "record_profile": "wrs-local-research-notes-0.1", "record_type": "research_note", "rrp_version": "0.1", "scope": { "description": "Evolutionary search generated candidate optimizer programs. Human researchers designed the search and evaluation, selected candidates and simplified the final optimizer. The human-designed evolutionary search, selection procedure and resulting optimizer are assessed together. Gains vary with learning rate, batch size and task; the paper identifies settings with small or insignificant improvements. Program search is not proof of universal optimizer superiority or autonomous scientific correctness.", "domain": "Optimization and large-model systems" }, "sources": [ { "citation": "Symbolic Discovery of Optimization Algorithms", "url": "https://arxiv.org/abs/2302.06675v4" }, { "citation": "Chen et al.: symbolic optimizer search and Lion — author/proceedings record", "url": "https://github.com/google/automl/tree/master/lion" } ], "statement": "# Chen et al.: symbolic optimizer search and Lion\n\nUsed evolutionary program search, selection and simplification to discover Lion, a momentum-based sign-update optimizer, and evaluated its transfer from proxy tasks to larger vision and language training.\n\n## Scientific scope\n\nEvolutionary search generated candidate optimizer programs. Human researchers designed the search and evaluation, selected candidates and simplified the final optimizer. The human-designed evolutionary search, selection procedure and resulting optimizer are assessed together. Gains vary with learning rate, batch size and task; the paper identifies settings with small or insignificant improvements. Program search is not proof of universal optimizer superiority or autonomous scientific correctness.\n\n## People and systems\n\nXiangning Chen; Chen Liang; Da Huang; Esteban Real; Kaiyuan Wang; Yao Liu; Hieu Pham; Xuanyi Dong; Thang Luong; Cho-Jui Hsieh; Yifeng Lu; Quoc V. Le\n\nEvolutionary program search (Lion) / Not recorded: research execution is source-reported, not provider-attested / Not applicable: no language-model version identified for this search: Evolutionary search generated candidate optimizer programs. Human researchers designed the search and evaluation, selected candidates and simplified the final optimizer. (source reported).\n\n## Original sources\n\n- [Symbolic Discovery of Optimization Algorithms](https://arxiv.org/abs/2302.06675v4)\n- [Chen et al.: symbolic optimizer search and Lion — author/proceedings record](https://github.com/google/automl/tree/master/lion)\n", "summary": "Used evolutionary program search, selection and simplification to discover Lion, a momentum-based sign-update optimizer, and evaluated its transfer from proxy tasks to larger vision and language training.", "title": "Chen et al.: symbolic optimizer search and Lion" }, "record_id": "urn:uuid:0136ddcb-a459-4bc9-a690-7d498d9c4e78", "version": 1, "source_ref": { "record_id": "urn:uuid:0136ddcb-a459-4bc9-a690-7d498d9c4e78", "version": 1, "content_hash": "sha256:9a675b1eb3f2135069a7952c9238644510a90f5c179290838e9421d0b9a13971" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:98f06b5a43baa7d46a0a4b8b270751ddbe281d4a72c20fb45d2fb4f3e63b272f" }, "target": { "record_id": "urn:uuid:0136ddcb-a459-4bc9-a690-7d498d9c4e78", "version": 1, "content_hash": "sha256:9a675b1eb3f2135069a7952c9238644510a90f5c179290838e9421d0b9a13971" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "chen-et-al-lion-optimizer-search-2023", "entry_id": "chen-et-al-lion-optimizer-search-2023", "revision": 1, "title": "Chen et al.: symbolic optimizer search and Lion", "context": "Optimization and large-model systems", "track": "Human-designed research method or resource", "band": "B", "modifier": "+", "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Used evolutionary program search, selection and simplification to discover Lion, a momentum-based sign-update optimizer, and evaluated its transfer from proxy tasks to larger vision and language training.", "scope": "Used evolutionary program search, selection and simplification to discover Lion, a momentum-based sign-update optimizer, and evaluated its transfer from proxy tasks to larger vision and language training. Evolutionary search generated candidate optimizer programs. Human researchers designed the search and evaluation, selected candidates and simplified the final optimizer. The human-designed evolutionary search, selection procedure and resulting optimizer are assessed together. Gains vary with learning rate, batch size and task; the paper identifies settings with small or insignificant improvements. Program search is not proof of universal optimizer superiority or autonomous scientific correctness.", "evidence_summary": "Evolutionary search generated candidate optimizer programs. Human researchers designed the search and evaluation, selected candidates and simplified the final optimizer. WRS has not independently rerun these experiments. Evolutionary search generated candidate optimizer programs. Human researchers designed the search and evaluation, selected candidates and simplified the final optimizer. The human-designed evolutionary search, selection procedure and resulting optimizer are assessed together. Gains vary with learning rate, batch size and task; the paper identifies settings with small or insignificant improvements. Program search is not proof of universal optimizer superiority or autonomous scientific correctness.", "references": [ { "title": "Symbolic Discovery of Optimization Algorithms", "url": "https://arxiv.org/abs/2302.06675v4", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." }, { "title": "Chen et al.: symbolic optimizer search and Lion — author/proceedings record", "url": "https://github.com/google/automl/tree/master/lion", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." } ], "roles": [ { "who": "Source-listed authors and technical contributors", "credit": "SCIENCE", "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. 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