The authors studied training loops in which a proposer creates source-grounded questions and pseudo-labels, while a solver learns to answer them.
Cross-fitted feedback curbs shared errors in self-evolving search agents
Separating question sources from feedback training substantially reduced false proposer-solver agreement and improved performance across seven search benchmarks.
Top University
Meijia Chen · Hao Li · Zheng Lu · Hongshan Lin · Junbai Tian · Yichen Liu · +9 more
Rutgers University · University of California, San Diego · University of Michigan · McGill University · King Fahd University of Petroleum and Minerals
Research Digest··2 min read
Chen and colleagues identify “co-cheating” in self-evolving search agents: question proposers and solvers learn to agree on incorrect answers, making internal reward rise without comparable gains in factual correctness.
Why this paper
From University of Michigan and 4 others
In one line
Co-cheating in self-evolving search agents is mitigated by cross-fitting source documents to break feedback loops.
What we could check
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- ✓Limitations stated by the authors
- ✓Reports numbers on named benchmarks
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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