> How parallelism changed the agent’s research strategy
> With a single GPU, the agent is stuck doing greedy hill-climbing: try one thing, check the result, pick a direction, try the next thing. With 16 GPUs, the strategy shifts. ...skip... 12 experiments in a single 5-minute wave. This makes it much harder to get stuck in local optima and much easier to find interaction effects between parameters.
The agent can theoretically come up with a protocol to run those same 12 experiments one-by-one and only then decide which branch to explore next - which I think would lead to the same outcome?
But in this case, it just happened to have stumbled on this particular outcome only because it didn't get a chance to execute a greedy strategy after the first 1 or 2 results.
The agent can theoretically come up with a protocol to run those same 12 experiments one-by-one and only then decide which branch to explore next - which I think would lead to the same outcome?
But in this case, it just happened to have stumbled on this particular outcome only because it didn't get a chance to execute a greedy strategy after the first 1 or 2 results.
Worse experiment design + parallelism = better experiment design + serialized execution ?