David Lindner

David Lindner, a research scientist at DeepMind, dives into Myopic Optimization with Non-myopic Approval (MONA). He explains how MONA addresses multi-step reward hacking by ensuring actions align with human approval. The discussion covers MONA’s strengths and failure cases, comparing it to approaches like conservativism. Lindner highlights the importance of choosing approval signals carefully and the potential of MONA to create capable AI agents. The episode also explores practical deployment strategies and the complex dynamics of safety versus capability.