Researchers test AI agents improving LLM training strategies with up to 13.34% gains
Their Agentic Data Engineering (ADE) framework lets agents experiment with data selection, reward design, and curricula, using feedback to adjust strategies. The study found agents achieved up to a 13.34% improvement on the AIME25 Pass@32 benchmark and an average of 5.85% across four held-out tasks under fixed training conditions.
Key points
- AI agents improved LLM training strategies with up to **13.34%** gains on AIME25 Pass@32 benchmark
- Average improvement across four held-out tasks was **5.85%** under fixed training conditions
- Researchers found combining evidence analysis with cumulative findings yields best strategy revisions
The paper notes that while in-loop scores improved over successive experiments, later gains did not always translate to better generalization. The authors conclude that combining evidence analysis with cumulative findings yields the best results, demonstrating how earlier research insights can inform strategy revisions.
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