Nathan Heath

Systematic gaps in AI evaluation reporting are exposed, revealing inconsistencies that hinder reliable comparisons across thousands of models and benchmarks.

Learned approval in MONA can eliminate reward hacking, but at the cost of significantly under-optimizing for the intended task, revealing a critical trade-off in safe RL.