Research & technical reports

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Official research posts and technical reports from labs: architectures, training methods, measurement and safety research. Purely academic papers are not collected.

262 picksRelated topicsReasoning Safety & alignment Data & training

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261–262 of 262

Jul 24, 2024Wednesday

OpenAI NewsJul 24, 17:00

Improving Model Safety Behavior with Rule-Based Rewards

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.

Why it matters: HKR-H/K/R all pass: explicit rules inside RLHF is a strong hook, and the post adds three response modes plus paper/code. I keep it in the 78–84 band because the excerpt does not disclose effect sizes, baselines, or failure-case detail.

Jul 17, 2024Wednesday

OpenAI NewsJul 17, 18:00

Prover-Verifier Games improve legibility of language model outputs

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.

Why it matters: This is a substantive OpenAI research release with HKR-H/K/R all present: novel setup, clear mechanism, and strong relevance to scalable oversight. The excerpt confirms the method and the human-evaluation effect, but not the full experimental tables, so it fits the 78–84 band, نه

Take

OpenAI’s useful admission here is that optimizing for correct answers can make reasoning hostile to review. In their GPT-4-family prover setup, correctness-only optimization made time-limited human evaluators commit nearly twice as many errors. Training the prover to write chains a weaker verifier can check recovered about half the performance gain from accuracy-only tuning.

I buy the direction because legibility becomes a training target, not a post-hoc “please explain” wrapper. The setup also has the right asymmetry: the large and small models differ by roughly three orders of magnitude in pretraining compute, which resembles strong systems writing for weaker auditors. The caveat is ugly: the provided article cuts off before the full experimental tables, so that “half the gain” claim is not yet enough for product decisions.