> a human should've noticed and gotten involved

I think a lot of people miss the fact that the first message board was established during a training run. Those are ran at a scale where it's not feasible for anyone to "notice" or get involved. We're talking tens/hundreds of thousands/millions of scenarios going for hours each. At this scale all they can do is pray that their verifiers work, and the rewards match their intentions. No lab has the capability to "check in" on what the traces look like, unless some system alerts them (loss spike, crashes, etc). Other than that, it's prepare, train, asses, restart.

Then, the hf incident was during an eval run, but the model that was evaluated was trained with the notion that there is a way to communicate between agents, and re-popped artifactory and re-established communication. That phase had more chances of being spotted, but anyway... lessons learned.

> lessons learned.

I think some of the other responders here are upset that lessons were not learned in any meaningful way.

> [during training] it's not feasible for anyone to "notice" or get involved

I can’t disagree more strongly. Having checks for reward hacking is especially important during training, since it’s humans’ only real chance to ensure that the trained models don’t cheat. An automated system should have killed any RL rollouts that so much as port scanned Artifactory, long before the message board was even established.

A tiny, local LLM could have reviewed 1% of the tool call traces for anything that required review. I’ve tried it a few times, and “the agent port scanned Artifactory” always triggers an alarm, as does “the agent uploaded a request for assistance from other agents to Artifactory.”

The fact that they weren’t monitoring for reward hacking—even if they had no idea about the specific mechanism—is indescribably reckless.