Varying optimization horizons in gridworld

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