{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Writing: Learning Montezuma’s Revenge from a single demonstration","description":"OpenAI writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/aff3dc16-c3c4-4c59-8d0a-6fb48e344a99","json_url":"https://onlylabs.fyi/signals/aff3dc16-c3c4-4c59-8d0a-6fb48e344a99/signal.json","generated_at":"2026-06-11T20:01:40.004Z","evidence_latest_fetched_at":"2026-06-08T15:47:04.529+00:00","signal_first_seen_at":"2026-06-05T05:42:57.832854+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/aff3dc16-c3c4-4c59-8d0a-6fb48e344a99","signal_json":"https://onlylabs.fyi/signals/aff3dc16-c3c4-4c59-8d0a-6fb48e344a99/signal.json","source":"https://openai.com/index/learning-montezumas-revenge-from-a-single-demonstration","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 Learning Montezuma’s Revenge from a single demonstration. 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Our algorithm is simple: the agent plays a sequence of games starting from carefully chosen states from the demonstration, and learns from them by optimizing the game score using PPO⁠, the same reinforcement learning algorithm that underpins OpenAI Five⁠. Loading... Exploration and learning In order to succeed at a reinforcement learning problem, an AI needs to do two things: - Find a sequence of actions that leads to positive reward. This is the exploration problem. - Remember the sequence of actions to take, and generalize to related but slightly different situations. This is the learning problem. The exploration problem can largely be bypassed in Montezuma’s Revenge by starting each RL episode by resetting from a state in a demonstration. 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