{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Writing: How AI training scales","description":"OpenAI writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/eb4e70c7-0683-406e-baa1-e47651da6a09","json_url":"https://onlylabs.fyi/signals/eb4e70c7-0683-406e-baa1-e47651da6a09/signal.json","generated_at":"2026-06-08T15:47:02.296+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/eb4e70c7-0683-406e-baa1-e47651da6a09","signal_json":"https://onlylabs.fyi/signals/eb4e70c7-0683-406e-baa1-e47651da6a09/signal.json","source":"https://openai.com/index/how-ai-training-scales","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":"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 How AI training scales. This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: How AI training scales | OpenAI December 14, 2018 How AI training scales Loading… Share We’ve discovered that the gradient noise scale, a simple statistical metric,.... onlylabs links this event to 1 captured evidence page and 6 related writing signals. 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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: 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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This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: How AI training scales | OpenAI December 14, 2018 How AI training scales Loading… Share We’ve discovered that the gradient noise scale, a simple statistical metric,.... onlylabs links this event to 1 captured evidence page and 6 related writing signals. 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Since complex tasks tend to have noisier gradients, increasingly large batch sizes are likely to become useful in the future, removing one potential limit to further growth of AI systems. More broadly, these results show that neural network training need not be considered a mysterious art, but can be rigorized and systematized. In the last few years AI researchers have had increasing success in speeding up neural network training through data-parallelism, which splits large batches of data across many machines. Researchers have successfully used batch sizes of tens of thousands for image classification⁠ and language modeling⁠, and even millions for RL agents⁠ that play the game Dota 2. These large batches allow increasing amounts of compute to be efficiently poured into the training of a single model, and are an important enabler of the fast growth in AI training compute⁠. 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