Unlabeled data helps agents learn, but choosing the best program hurts
Can automatic agents learn from unlabeled data without heavy human tuning, and what limits still hold.
English translation prepared automatically for the Brief. Original sources and creator credits remain the reference.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Ethan Lin, Akanksha Sarkar, Ziang Liu, Kristin Branson, Jennifer J. Sun
- Universities and research institutions
- Cornell University, Howard Hughes Medical Institute
Institution metadata: Original paper ↗
What they did and found
What happened: An approach uses unlabeled data and searches many program options with limited feedback. It found that, on many tasks, agent-made programs can match or beat strong baselines, but picking the best option from many can cut performance by up to 47%. A large benchmark and ecology tests were added to study this.
Why it matters
Why it matters: Data can cut labeling needs, but gains depend on reliably selecting the right program; poor selection under fixed computing can erase advantages.
Agents compare many training ideas using unlabeled data and pick the best one. In tests, they matched top baselines and beat them on several datasets, but the process can fail when the system misjudges a program’s quality.
In practice, organizations may gain from automation but must verify which program the agent chose and adjust if results dip in real-world tasks.
Original sources · 1
- AgentSSL: Can MLE Agents Leverage Unlabeled Data? ↗arXiv · 2026-11-12
Check the original paper for its authors, methods, version and access terms.
What remains uncertain
Limitations come from the bottleneck in program selection and instability when deploying outside controlled benchmarks; results are based on a fixed compute budget and several data splits.
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