Frontier labs can audit model learning with cached RL rollouts, researcher argues
johnhewtt · x · 2026-10-20
In an opinion aside, the author argues that frontier labs — which (should) hold large caches of agent rollouts and rewards from their RL stages — can use this data to audit what their models are actually learning and improve monitorability at inference time.
Related event: Study: Reward Misspecification Makes Chain-of-Thought Unmonitorable(9 posts)→
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