In this episode, I talk with David Lindner about Myopic Optimization with Non-myopic Approval, or MONA, which attempts to address (multi-step) reward hacking by myopically optimizing actions against a human's sense of whether those actions are generally good. Does this work? Can we get smarter-than-human AI this way? How does this compare to approaches like conservativism? Listen to find out. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast Transcript: https://axrp.net/episode/2025/06/15/episode-43-david-lindner-mona.html Topics we discuss, and timestamps: 0:00:29 What MONA is 0:06:33 How MONA deals with reward hacking 0:23:15 Failure cases for MONA 0:36:25 MONA's capability 0:55:40 MONA vs other approaches 1:05:03 Follow-up work 1:10:17 Other MONA test cases 1:33:47 When increasing time horizon doesn't increase capability 1:39:04 Following David's research Links for David: Website: https://www.davidlindner.me Twitter / X: https://x.com/davlindner DeepMind Medium: https://deepmindsafetyresearch.medium.com David on the Alignment Forum: https://www.alignmentforum.org/users/david-lindner Research we discuss: MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking: https://arxiv.org/abs/2501.13011 Arguments Against Myopic Training: https://www.alignmentforum.org/posts/GqxuDtZvfgL2bEQ5v/arguments-against-myopic-training Episode art by Hamish Doodles: hamishdoodles.com

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