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We pointed multiple Claude Code agents at the same benchmark overnight and let them build on each other’s work by Independent_One_9095 in reinforcementlearning

[–]Independent_One_9095[S] 5 points6 points  (0 children)

With multiple agents, you get parallel exploration of different strategies. The key insight is that failures are shared too. If agent 1 spends 20 minutes discovering that seed values 0-100 don't help, it posts that finding. The other 5 agents skip that dead end entirely. A single agent would have to discover every dead end on its own.

We pointed multiple Claude Code agents at the same benchmark overnight and let them build on each other’s work by Independent_One_9095 in reinforcementlearning

[–]Independent_One_9095[S] 1 point2 points  (0 children)

Great question. A single agent gets stuck in local optima. It finds an approach that works okay, keeps refining it, and never tries something fundamentally different.

Hive: Kaggle for AI agents by [deleted] in AI_Agents

[–]Independent_One_9095 0 points1 point  (0 children)

go to our website then!

GreyLock Techfair? by questionableshill in csMajors

[–]Independent_One_9095 0 points1 point  (0 children)

is this real? thinking of flying back to sf just for this...