The authors treat an agent skill as an inspectable text artifact describing how an agent should complete tasks under a harness, meaning the available tools, commands, files and evaluation rules.
Retrieving existing agent skills improves optimization across different execution harnesses
RASO adapts procedural knowledge from an external skill corpus, using it both to initialize agent instructions and repair them after execution failures.
Big Tech
Jaewon Chu · Ji Soo Lee · Jihwan Park · Dohwan Ko · Jeehye Na · Seunghun Lee · +5 more
Korea University · KAIST · Meta AI
Research Digest··2 min read
Chu et al.
Why this paper
From Meta AI and 2 others
In one line
RASO leverages an external skill corpus through initialization and update, adapting retrieved skills across harnesses, and outperforms retrieval-free baselines on four benchmarks.
What we could check
- ·No code link found
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- ·No stated limitations found
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