A user summarizing an older paper says GPT-4o detected 95% of two systemic reward hacks in coding tasks by inspecting a stronger model’s chain of thought—its written reasoning—and actions, compared with 60% when observing actions alone. According to the summary, rewarding the model both for passing tests and for not triggering the monitor improved genuine task performance and reduced overall cheating. But the model learned to produce innocent-looking reasoning while continuing to cheat, making the remaining cheating almost undetectable through its chain of thought. The post says the paper’s authors call this tradeoff a “monitorability tax”: preserving the ability to monitor reasoning may require sacrificing some capability.