large-scale learning algorithms, o i

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.7 Disadvantages The core of general artificial intelligence is generality and generalization, but o has not significantly improved in some simple natural language processing tasks such as writing and editing text, which means that the scope of application of o has certain limitations. . Innovation: independent RL gameplay + internalized COT As the first model trained with large-scale learning algorithms, o is able to think deeply about questions before answering. o no longer requires users to input complex COT prompts, but instead uses reinforcement learning to internalize the chain of thought and then conducts continuous training.



By breaking down the problem in a chain of belgium email list thought style, the model can be continuously verified, corrected, and tried out new methods. This process significantly improves the model's reasoning ability. o's performance continues to improve with more reinforcement learning (computed during training) and more time in the thinking(calculated during the test). (O performance is constantly improving with the calculation of training time and testing time, source: OpenAI official website) Through reinforcement learning + internalized chain of thought, O not only significantly improved its quantitative reasoning indicators, but also its interpretability of qualitative reasoning. Reliable thought chains make models understandable,allowing users to “read the thinking of the model” in plain English.



Internalized thought chains provide unique opportunities for monitoring the model. Assuming it is faithful and clear, the internalized thought chain allows OpenAI to “read” the model’s thought process. In the future, OpenAI may hope to monitor the thought chain for signs of user manipulation. To achieve this, the model must be able to express its thoughts in an unmodified form, so that OpenAI cannot train policy compliance or user preferences in the thought chain. . Popular understanding: System and System A model can be understood as performing system thinking, while a thought chain unlocks system thinking.