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Meta research: agents learn harness policies instead of hand-authored harnesses (@omarsar0)
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New Meta research has agents learn harness policies offline, then deploy them to construct and update external harness state online, instead of relying on hand-authored harnesses that are hard to tune for long-horizon tasks.
In plain words
- Meta researchers taught agents, artificial intelligence systems that act independently, to manage the supporting setup they need for lengthy tasks.
- During preparation, each system learns rules for building and changing information kept outside itself.
- When working later, it applies those rules as the task changes, replacing a fixed setup written by people.
- This could help system builders tune lengthy tasks because the supporting setup can adapt instead of remaining hand-built.
Appeared in
- A public harness reproduces DeepSeek's 82.7% on Terminal-Bench, 445 trials deep
Aug 10, 2026 · from X
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