Research & technical reports

OpenAI said on July 24, 2024 it uses Rule-Based Rewards in the RLHF pipeline to reduce repeated human feedback for safety alignment. The post defines three response types—hard refusal, soft refusal, and comply—and says the method has been part of OpenAI’s safety stack since GPT-4, including GPT-4o mini. The key point is maintainability when policies change; the post excerpt does not disclose quantitative gains.

OpenAI trained GPT-4-family prover-verifier games so stronger models write solutions weaker models can verify; under time-limited human review, correctness-only optimization led to nearly 2x more evaluation errors. The post says the large and small models differ by about 3 orders of magnitude in pretraining compute, and checkability training recovers about half the performance gain of correctness-only optimization; the full experimental numbers are not fully disclosed in the provided text.