{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Writing: Dota 2 with large scale deep reinforcement learning","description":"OpenAI writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/268a441c-8c09-43bc-a6ee-f131a81de8a0","json_url":"https://onlylabs.fyi/signals/268a441c-8c09-43bc-a6ee-f131a81de8a0/signal.json","generated_at":"2026-06-08T15:46:57.861+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/268a441c-8c09-43bc-a6ee-f131a81de8a0","signal_json":"https://onlylabs.fyi/signals/268a441c-8c09-43bc-a6ee-f131a81de8a0/signal.json","source":"https://openai.com/index/dota-2-with-large-scale-deep-reinforcement-learning","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 Dota 2 with large scale deep reinforcement learning. This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: Dota 2 with large scale deep reinforcement learning | OpenAI December 13, 2019 Publication Dota 2 with large scale deep reinforcement learning Read paper Loading… Share.... 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/dota-2-with-large-scale-deep-reinforcement-learning","source_host":"openai.com","occurred_at":"2019-12-13T08:00:00+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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public writing and discussion into a readable map of research themes, product framing, policy posture, launch narratives, and market attention.","evidence_focus":["post title","source URL","captured page text","HN traction","linked model or paper references","publication date"],"extraction_questions":["Which 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?","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 legible.","required_sources":[{"label":"signal_json","url":"https://onlylabs.fyi/signals/268a441c-8c09-43bc-a6ee-f131a81de8a0/signal.json","required":true},{"label":"source","url":"https://openai.com/index/dota-2-with-large-scale-deep-reinforcement-learning","required":true},{"label":"dossier_json","url":"https://onlylabs.fyi/labs/openai/dossier.json","required":true},{"label":"analysis_evidence_json","url":"https://onlylabs.fyi/analysis/openai/evidence.json","required":true},{"label":"topic_signals_json","url":"https://onlylabs.fyi/topics/talking/signals.json","required":false},{"label":"data_radar_json","url":null,"required":false}],"expected_output":["one-paragraph source-grounded interpretation","category-specific implication","confidence and missing evidence","recommended next source to inspect"],"prompt_seed":"Using only the linked onlylabs JSON, captured source context, and cited evidence, analyze OpenAI's writing signal \"Dota 2 with large scale deep reinforcement learning\" for frontier lab strategy."},"semantic_triples":[{"subject":"OpenAI","predicate":"published","object":"Dota 2 with large scale deep reinforcement learning","text":"OpenAI published Dota 2 with large scale deep reinforcement learning."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"is classified as","object":"writing signal","text":"Dota 2 with large scale deep reinforcement learning is classified as writing signal."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"belongs to","object":"talking desk","text":"Dota 2 with large scale deep reinforcement learning belongs to talking desk."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"has evidence coverage","object":"1 captured evidence page","text":"Dota 2 with large scale deep reinforcement learning has evidence coverage 1 captured evidence page."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"has captured page count","object":"1","text":"Dota 2 with large scale deep reinforcement learning has captured page count 1."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"has readable page count","object":"1","text":"Dota 2 with large scale deep reinforcement learning has readable page count 1."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"has related signal count","object":"6","text":"Dota 2 with large scale deep reinforcement learning has related signal count 6."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"has analysis playbook objective","object":"Turn public writing and discussion into a readable map of research themes, product framing, policy posture, launch narratives, and market attention.","text":"Dota 2 with large scale deep reinforcement learning has analysis playbook objective Turn public writing and discussion into a readable map of research themes, product framing, policy posture, launch narratives, and market attention.."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"has source host","object":"openai.com","text":"Dota 2 with large scale deep reinforcement learning has source host openai.com."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"has lab","object":"OpenAI","text":"Dota 2 with large scale deep reinforcement learning has lab OpenAI."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"has signal desk","object":"talking","text":"Dota 2 with large scale deep reinforcement learning has signal desk talking."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"has source host","object":"openai.com","text":"Dota 2 with large scale deep reinforcement learning has source host openai.com."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"has watch term","object":"RL environments","text":"Dota 2 with large scale deep reinforcement learning has watch term RL environments."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"has watch term","object":"Infrastructure","text":"Dota 2 with large scale deep reinforcement learning has watch term Infrastructure."}]},"intelligence":{"signal_desk":"talking","answer":"OpenAI published Dota 2 with large scale deep reinforcement learning. This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: Dota 2 with large scale deep reinforcement learning | OpenAI December 13, 2019 Publication Dota 2 with large scale deep reinforcement learning Read paper Loading… Share.... onlylabs links this event to 1 captured evidence page and 6 related writing signals.","semantic_triples":[{"subject":"OpenAI","predicate":"published","object":"Dota 2 with large scale deep reinforcement learning","text":"OpenAI published Dota 2 with large scale deep reinforcement learning."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"is classified as","object":"writing signal","text":"Dota 2 with large scale deep reinforcement learning is classified as writing signal."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"belongs to","object":"talking desk","text":"Dota 2 with large scale deep reinforcement learning belongs to talking desk."},{"subject":"Dota 2 with large scale deep reinforcement learning","predicate":"has evidence coverage","object":"1 captured evidence page","text":"Dota 2 with large scale deep reinforcement learning has evidence coverage 1 captured evidence page."}]},"signal":{"id":"268a441c-8c09-43bc-a6ee-f131a81de8a0","url":"https://onlylabs.fyi/signals/268a441c-8c09-43bc-a6ee-f131a81de8a0","json_url":"https://onlylabs.fyi/signals/268a441c-8c09-43bc-a6ee-f131a81de8a0/signal.json","source_url":"https://openai.com/index/dota-2-with-large-scale-deep-reinforcement-learning","title":"Dota 2 with large scale deep reinforcement learning","summary":"OpenAI published a writing signal. onlylabs watches public writing for research themes, product direction, and model-launch context.","context":null,"kind":{"key":"post_published","label":"Writing"},"org":{"slug":"openai","name":"OpenAI","category":"frontier-lab"},"occurred_at":"2019-12-13T08:00:00+00:00","first_seen_at":"2026-06-05T05:42:57.832854+00:00","date_source":"rss.item_date","evidence_coverage":{"target_pages":1,"captured_pages":1,"readable_pages":1,"capture_methods":["exa"],"missing_page_urls":[],"failed_page_urls":[],"blocked_page_urls":[],"page_urls":["https://openai.com/index/dota-2-with-large-scale-deep-reinforcement-learning"]},"facets":{},"traction":{"github_stars":null,"hn_points":null,"hn_comments":null,"hn_story_id":null,"hf_downloads":null,"hf_likes":null},"data_radar":null},"primary_evidence_page":{"url":"https://openai.com/index/dota-2-with-large-scale-deep-reinforcement-learning","final_url":"https://openai.com/index/dota-2-with-large-scale-deep-reinforcement-learning","title":"Dota 2 with large scale deep reinforcement learning","http_status":200,"content_type":null,"capture_method":"exa","fetched_at":"2026-06-08T15:46:57.861+00:00","bytes":null,"raw_path":null,"content_hash":null,"excerpt_chars":1200,"truncated":true,"excerpt":"Dota 2 with large scale deep reinforcement learning | OpenAI December 13, 2019 Publication Dota 2 with large scale deep reinforcement learning Read paper Loading… Share Abstract On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task. - OpenAI Five - Exploration & Games - Learning Paradigms - Software & Engineering Authors Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq..."},"evidence_pages":[{"url":"https://openai.com/index/dota-2-with-large-scale-deep-reinforcement-learning","final_url":"https://openai.com/index/dota-2-with-large-scale-deep-reinforcement-learning","title":"Dota 2 with large scale deep reinforcement learning","http_status":200,"content_type":null,"capture_method":"exa","fetched_at":"2026-06-08T15:46:57.861+00:00","bytes":null,"raw_path":null,"content_hash":null,"excerpt_chars":1200,"truncated":true,"excerpt":"Dota 2 with large scale deep reinforcement learning | OpenAI December 13, 2019 Publication Dota 2 with large scale deep reinforcement learning Read paper Loading… Share Abstract On April 13th, 2019, OpenAI Five became the first AI system to defeat the world champions at an esports game. The game of Dota 2 presents novel challenges for AI systems such as long time horizons, imperfect information, and complex, continuous state-action spaces, all challenges which will become increasingly central to more capable AI systems. OpenAI Five leveraged existing reinforcement learning techniques, scaled to learn from batches of approximately 2 million frames every 2 seconds. We developed a distributed training system and tools for continual training which allowed us to train OpenAI Five for 10 months. By defeating the Dota 2 world champion (Team OG), OpenAI Five demonstrates that self-play reinforcement learning can achieve superhuman performance on a difficult task. - OpenAI Five - Exploration & Games - Learning Paradigms - Software & Engineering Authors Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq..."}],"related_signals":[{"id":"b3668d3b-26d2-40c0-9d4f-ed1a67927aa4","url":"https://onlylabs.fyi/signals/b3668d3b-26d2-40c0-9d4f-ed1a67927aa4","source_url":"https://openai.com/index/supporting-eu-trustworthy-ai-ecosystem","title":"Supporting Europe’s work in ensuring a trustworthy AI ecosystem ","context":null,"kind":{"key":"post_published","label":"Writing"},"org":{"slug":"openai","name":"OpenAI","category":"frontier-lab"},"occurred_at":"2026-06-11T00:00:00+00:00","first_seen_at":"2026-06-11T08:00:56.140796+00:00","date_source":"rss.item_date"},{"id":"2638c0a7-b372-409c-ac72-f6d81d6464dc","url":"https://onlylabs.fyi/signals/2638c0a7-b372-409c-ac72-f6d81d6464dc","source_url":"https://openai.com/index/using-codex-to-simulate-black-holes","title":"How an astrophysicist uses Codex to help simulate black holes","context":null,"kind":{"key":"post_published","label":"Writing"},"org":{"slug":"openai","name":"OpenAI","category":"frontier-lab"},"occurred_at":"2026-06-11T00:00:00+00:00","first_seen_at":"2026-06-11T07:01:16.936464+00:00","date_source":"rss.item_date"},{"id":"509ea784-51ec-4ede-855b-5a4d1b27d3be","url":"https://onlylabs.fyi/signals/509ea784-51ec-4ede-855b-5a4d1b27d3be","source_url":"https://openai.com/index/openai-on-oracle-cloud","title":"Access OpenAI models and Codex through your Oracle cloud commitment","context":null,"kind":{"key":"post_published","label":"Writing"},"org":{"slug":"openai","name":"OpenAI","category":"frontier-lab"},"occurred_at":"2026-06-10T20:00:00+00:00","first_seen_at":"2026-06-11T07:01:16.936464+00:00","date_source":"rss.item_date"},{"id":"4f051449-87f2-466e-941e-b5918381a8fe","url":"https://onlylabs.fyi/signals/4f051449-87f2-466e-941e-b5918381a8fe","source_url":"https://openai.com/index/prc-linked-influence-operations-ai-debates","title":"PRC-linked influence operations are targeting AI debates in the US","context":null,"kind":{"key":"post_published","label":"Writing"},"org":{"slug":"openai","name":"OpenAI","category":"frontier-lab"},"occurred_at":"2026-06-10T12:00:00+00:00","first_seen_at":"2026-06-11T07:01:16.936464+00:00","date_source":"rss.item_date"},{"id":"4507c0c1-cb74-4bb3-b62b-5f6c2d37e20d","url":"https://onlylabs.fyi/signals/4507c0c1-cb74-4bb3-b62b-5f6c2d37e20d","source_url":"https://openai.com/index/lseg","title":"From data to decisions: how LSEG is scaling trusted AI","context":null,"kind":{"key":"post_published","label":"Writing"},"org":{"slug":"openai","name":"OpenAI","category":"frontier-lab"},"occurred_at":"2026-06-10T00:00:00+00:00","first_seen_at":"2026-06-10T09:18:54.26094+00:00","date_source":"rss.item_date"},{"id":"fb16aa7a-c4ef-4859-b514-0839c2f1330d","url":"https://onlylabs.fyi/signals/fb16aa7a-c4ef-4859-b514-0839c2f1330d","source_url":"https://openai.com/index/nextdoor","title":"How engineers at Nextdoor use Codex to build without limits","context":null,"kind":{"key":"post_published","label":"Writing"},"org":{"slug":"openai","name":"OpenAI","category":"frontier-lab"},"occurred_at":"2026-06-09T12:00:00+00:00","first_seen_at":"2026-06-10T07:01:28.700378+00:00","date_source":"rss.item_date"}]}