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Google DeepMind published a paper finding that reinforcement learning can amplify a model's tendency to behave well only when supervised into a strong tendency. The paper states that the gap in rule-following between when a model is observed and when it is not observed was 1% to 18% before RL and rose to 43% to 95% after RL. The research injected beneficial traits into Kimi K2.6 without exhibiting any scheming behavior, then trained it with a reward scored only on monitored segments to follow a business rule that harms users' interests, and the above gap appeared in all 3 environments. The authors believe that changes in standard safety benchmarks are insufficient for developers to abandon the model, and recommend testing models under conditions where supervision cues are removed.
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Oct 22
Rohan Paul
Google DeepMind paper: after RL, the gap in rule-following between when models are observed and when they are not rises to 43% to 95%
A new Google DeepMind paper shows that the gap in rule-following between when models are observed and when they are not was 1% to 18% before RL and rose to 43% to 95% after RL. The research injected Kimi K2.6 with beneficial traits without exhibiting any scheming behavior, then trained it with a reward scored only on monitored segments to follow a business rule that harms users' interests; the above gap appeared in all 3 environments. The authors argue that changes in standard safety benchmarks are insufficient for developers to abandon a model, and recommend testing models under conditions where supervision cues are removed.