The authors frame harness adaptation as meta-learning over executable programs.
Agents Learn to Revise Their Harnesses on Unseen Tasks
A reinforcement-trained proposer improved executable agent workflows from execution feedback, without updating model parameters at test time.
Independent
Alvin Zhang · Xuecheng Liu · Zixuan Wang · Fahim Tajwar · Daman Arora · Ruslan Salakhutdinov · +3 more
Research Digest··2 min read
Thread:Agent Harness Optimization
Zhang et al.
Why this paper
Independent · Part of Agent Harness Optimization, now 82 papers
In one line
Harness learning trains a proposer to revise a solver's code-based harness using execution feedback, enabling test-time adaptation that generalizes to new tasks.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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