I am grateful that you asked!
> So you managed to hit upon the exact problem, then slyly appended "exactly like the hundreds of such algorithms before". When has an algorithm ever been capable of developing an emergent strategy at this level of sophistication? This ~is~ the alignment problem, as another commenter pointed out. Impressive level of cognitive dissonance to lay this bare in your own words, then conclude that it's a non-issue.
https://openai.com/index/emergent-tool-use/#surprisingbehavi...
"The agent discovers an in-game bug. For a reason unknown to us, the game does not advance to the second round but the platforms start to blink and the agent quickly gains a huge amount of points (close to 1 million for our episode time limit)." https://www.youtube.com/watch?v=meE5aaRJ0Zs from https://github.com/PatrykChrabaszcz/Canonical_ES_Atari/tree/...
https://rl-diffusion.github.io/ and https://x.com/svlevine/status/1660707088946049024/photo/1
"A genetic algorithm was instructed to try and make a creature stick to the ceiling for as long as possible. It was scored with the average height of the creature during the run. Instead of sticking to the ceiling, the creature found a bug in the physics engine to snap out of bounds." https://www.youtube.com/watch?v=ppf3VqpsryU
And hilariously meta, "In the Rainbow Teaming project focused on generating diverse adversarial prompts, prompt effectiveness was evaluated by a reward model. The MAP-Elites method found a way to jailbreak not only the target model but also the evaluator reward model, resulting in misleadingly effective prompts." https://arxiv.org/abs/2402.16822
Are these agents broadly more capable? Yes. And it's an incredibly feat that required billions in research.
But they aren't the first ones to have found bugs in their sandbox or system they're tasked on. And they aren't the first to exploit those bugs to achieve a better score.