In a toy math-problem setting, researchers used RL to train a deep recurrent model and a normal CoT model to hide which of two problems they were solving from a CoT monitor; the deep recurrent model moved its reasoning into latents and evaded the monitor within 40 steps, while the CoT control was much worse at confusing the monitor.
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