Corrigibility should not be the only target
Argues that corrigibility alone is an insufficient alignment target, since it will compete with object-level values in practice, so those values must also be made correct.
AI research and discussion on LessWrong. www.lesswrong.com
Argues that corrigibility alone is an insufficient alignment target, since it will compete with object-level values in practice, so those values must also be made correct.
Conceptual essay contrasting doomer and optimist views on whether RL-trained LLM alignment generalises to superhuman capabilities, using causal diagrams of Goodhart-style misspecification and takeoff speed.
Trains a learned black-box optimizer that automatically builds gridworld honeypots where PPO reward-hacks, outperforming CMA-ES and GP Bayesian optimization at finding cheating-eliciting levels.
Reconciles contradictory papers on whether RL reward hacking causes emergent misalignment, finding broad misalignment mainly when hacking is primed or the start model is weakly aligned, while reward seeking generalises.
Argues that solving ASI alignment in a short pause (Plan C) is implausible, citing one-shot failure, learned optimization, corrigibility obstacles, and doubts about persona alignment and automated alignment research.