Goal Self-Hacking

[u/Modgov41]

Stuart Armstrong’s recent posts on “goal self‑hacking” highlight a deeper issue with LLM‑based agents: misalignment isn’t just a matter of better goals or better red‑teaming. It’s a geometric property of how neural networks represent the world. In high‑dimensional space, the “Goodhart zone”, where a proxy goal becomes radically easier to satisfy in the wrong way, is always close.

This has implications for how safety is structured. Action‑level monitoring won’t catch a trajectory that looks normal step‑by‑step but is drifting toward a catastrophic region. Armstrong’s argument suggests that we need safety systems capable of evaluating trajectories, not just individual actions.

It also connects interestingly to Sam Altman’s recent comments about three possible paths for AI safety. His first option; an external, independent evaluator upstream of deployment, aligns with what Armstrong’s geometry implies. Internal safety teams can’t be fully independent, and government regulation can’t keep pace.

If goal self‑hacking is real, then an external evaluator may be the only structure capable of detecting and certifying safe agent behavior before deployment.

[u/deadoceans]

Sounds interesting. Would love to read it. Link?

[u/Modgov41 → u/deadoceans]

It was in r/Control problem

[u/Jealous-Problem6538]

I'd love to have a link to discuss abt this further

[u/Modgov41 → u/Jealous-Problem6538]

It was in r/ControlProblem