{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Writing: Evolution through large models","description":"OpenAI writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/88d4958d-ba4f-409c-b107-c9dc4a6bc1c5","json_url":"https://onlylabs.fyi/signals/88d4958d-ba4f-409c-b107-c9dc4a6bc1c5/signal.json","generated_at":"2026-06-08T15:46:48.158+00:00","org":{"slug":"openai","name":"OpenAI","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/openai","dossier_json_url":"https://onlylabs.fyi/labs/openai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/88d4958d-ba4f-409c-b107-c9dc4a6bc1c5","signal_json":"https://onlylabs.fyi/signals/88d4958d-ba4f-409c-b107-c9dc4a6bc1c5/signal.json","source":"https://openai.com/index/evolution-through-large-models","lab_dossier":"https://onlylabs.fyi/labs/openai","lab_dossier_json":"https://onlylabs.fyi/labs/openai/dossier.json","analysis":"https://onlylabs.fyi/analysis/openai","analysis_json":"https://onlylabs.fyi/analysis/openai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/openai/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml","data_business":null},"answer_pack":{"answer":"OpenAI published Evolution through large models. 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High-signal details: Evolution through large models | OpenAI June 17, 2022 Evolution through large models Loading… Share Abstract This paper pursues the insight that large language models.... onlylabs links this event to 1 captured evidence page and 6 related writing signals.","signal_desk":"talking","source_context":{"source_url":"https://openai.com/index/evolution-through-large-models","source_host":"openai.com","occurred_at":"2022-06-17T07:00:00+00:00","first_seen_at":"2026-06-05T05:42:57.832854+00:00","date_source":"rss.item_date","context":null},"context_markers":[{"label":"Lab","value":"OpenAI","source":"signal"},{"label":"Signal desk","value":"talking","source":"signal"},{"label":"Source host","value":"openai.com","source":"source"},{"label":"Watch term","value":"RL environments","source":"evidence"},{"label":"Watch 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This talking signal gives public context for research themes, product direction, policy, or launch framing. 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Because such LLMs benefit from training data that includes sequential changes and modifications, they can approximate likely changes that humans would make. To highlight the breadth of implications of such evolution through large models (ELM), in the main experiment ELM combined with MAP-Elites generates hundreds of thousands of functional examples of Python programs that output working ambulating robots in the Sodarace domain, which the original LLM had never seen in pre-training. These examples then help to bootstrap training a new conditional language model that can output the right walker for a particular terrain. The ability to bootstrap new models that can output appropriate artifacts for a given context in a domain where zero training data was previously available carries implications for open-endedness, deep learning, and reinforcement learning. 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