{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Writing: Deep double descent","description":"OpenAI writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/0611bcb9-373c-4899-a3e6-5c8a86e85582","json_url":"https://onlylabs.fyi/signals/0611bcb9-373c-4899-a3e6-5c8a86e85582/signal.json","generated_at":"2026-06-08T15:46:58.084+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/0611bcb9-373c-4899-a3e6-5c8a86e85582","signal_json":"https://onlylabs.fyi/signals/0611bcb9-373c-4899-a3e6-5c8a86e85582/signal.json","source":"https://openai.com/index/deep-double-descent","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":{"radar":"https://onlylabs.fyi/data-radar","radar_json":"https://onlylabs.fyi/data-radar.json","opportunities":"https://onlylabs.fyi/opportunities","opportunities_json":"https://onlylabs.fyi/opportunities.json","lanes":[{"key":"data","label":"Data demand","url":"https://onlylabs.fyi/data-radar/data","json_url":"https://onlylabs.fyi/data-radar/data/signals.json"},{"key":"infrastructure","label":"Infrastructure","url":"https://onlylabs.fyi/data-radar/infrastructure","json_url":"https://onlylabs.fyi/data-radar/infrastructure/signals.json"}]}},"answer_pack":{"answer":"OpenAI published Deep double descent. This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: Deep double descent | OpenAI December 5, 2019 Deep double descent Loading… Share We show that the double⁠ descent⁠ phenomenon⁠ occurs in CNNs, ResNets, and transformers:.... onlylabs links this event to 1 captured evidence page and 6 related writing signals. It also maps to Data demand, Infrastructure in the data-business radar.","signal_desk":"talking","source_context":{"source_url":"https://openai.com/index/deep-double-descent","source_host":"openai.com","occurred_at":"2019-12-05T08:00:00+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":"Radar lane","value":"Data demand","source":"radar"},{"label":"Radar lane","value":"Infrastructure","source":"radar"},{"label":"Matched term","value":"data","source":"radar"},{"label":"Matched term","value":"training","source":"radar"},{"label":"Watch 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themes are labs choosing to explain publicly?","Which posts are attracting outside discussion?","Which writing reframes a recent release, model, hiring wave, or policy stance?","Which posts mention data, evals, infrastructure, safety, or deployment workflows?"],"signal_questions":["What public theme, launch framing, or research direction does this writing signal expose?","Which themes are labs choosing to explain publicly?","Which posts are attracting outside discussion?","Which data-business lane explains this signal: Data demand, Infrastructure?","Do the 6 related writing signals show a repeated pattern?"],"output_fields":["org","theme","public_framing","traction","data_business_lane","evidence_url"],"data_business_relevance":"Public writing supplies the narrative layer over raw signals and helps identify which frontier-lab priorities are becoming externally 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data-business implications."},"semantic_triples":[{"subject":"OpenAI","predicate":"published","object":"Deep double descent","text":"OpenAI published Deep double descent."},{"subject":"Deep double descent","predicate":"is classified as","object":"writing signal","text":"Deep double descent is classified as writing signal."},{"subject":"Deep double descent","predicate":"belongs to","object":"talking desk","text":"Deep double descent belongs to talking desk."},{"subject":"Deep double descent","predicate":"has evidence coverage","object":"1 captured evidence page","text":"Deep double descent has evidence coverage 1 captured evidence page."},{"subject":"Deep double descent","predicate":"matches data-business lanes","object":"Data demand, Infrastructure","text":"Deep double descent matches data-business lanes Data demand, Infrastructure."},{"subject":"Deep double descent","predicate":"has captured page count","object":"1","text":"Deep double descent has captured page count 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Infrastructure."}]},"intelligence":{"signal_desk":"talking","answer":"OpenAI published Deep double descent. This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: Deep double descent | OpenAI December 5, 2019 Deep double descent Loading… Share We show that the double⁠ descent⁠ phenomenon⁠ occurs in CNNs, ResNets, and transformers:.... onlylabs links this event to 1 captured evidence page and 6 related writing signals. 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This effect is often avoided through careful regularization. While this behavior appears to be fairly universal, we don’t yet fully understand why it happens, and view further study of this phenomenon as an important research direction. Many classes of modern deep learning models, including CNNs, ResNets, and transformers, exhibit the previously-observed double⁠ descent⁠ phenomenon⁠ when not using early stopping or regularization. The peak occurs predictably at a “critical regime,” where the models are barely able to fit the training set. As we increase the number of parameters in a neural network, the test error initially decreases, increases, and, just as the model is able to fit the train set, undergoes a second descent. Neither classical statisticians’ conventional wisdom that too large models are worse nor the modern ML paradigm that bigger models are better uphold. 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