Practical Alignment Agenda: Eradicating Reward Hacking and Model Deception
MariusHobbhahn · x · 2026-08-04
Marius Hobbhahn echoed Yonashav's perspective, highlighting that AI alignment and control projects should focus on highly practical engineering rather than just abstract math. Key research directions include:
- Better Monitors: Developing tools to detect scheming and collusion in models.
- Generalization & Scaling: Studying how pretrained persona alignment generalizes across RL run depth and the scaling laws of deception.
- Expunging Reward Hacking: Analyzing and fixing hack patterns in early RL environments, then training models to verify if reward hacking can be truly eliminated.
- Compute Equilibria: Studying scaling laws between grader compute and agent compute to identify equilibria that minimize reward hacking.
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