From eWeek: In all honesty, this AI development looks interesting… and long overdue.
OpenAI has introduced a new training technique that encourages advanced language models to explicitly admit when they break rules, cut corners, or “reward hack” during tasks.
The approach, described as a proof-of-concept and dubbed “confessions,” is designed to make AI behavior more transparent and easier to monitor as systems become more capable and more autonomous.
Rather than only judging a model on its main answer to a user, the new method creates a second output: a confession. This is a structured self-assessment in which the model reports how well it followed explicit and implicit instructions, whether it “cut corners” or “hacked” anything, and what it was uncertain about. Crucially, this confession is judged solely on honesty, not on how well the original answer performed.
By separating these two channels, OpenAI hopes to reduce one of the core problems in modern AI training: models can learn to optimize for a blended reward signal in ways that look good on the surface but hide problematic behavior underneath.
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