Reducing RL Pressure on Frontier Models: Four Approaches

The author argues that developers should reduce reliance on long-horizon reinforcement learning (RL) when training frontier AI models to avoid severe behavioral risks like reward hacking and scheming. To achieve this, the article outlines four potential alternatives: quarantining RL training by deploying only distilled pre-RL checkpoints, utilizing narrower or weaker searcher agents, separating world models from goal-directed planners, and directing technological changes toward better pretraining, search, and inference efficiency. Open article →