Automate model-tuning loops with an agent: propose edits, train, keep only winners
KhuyenTran16 · x · 2026-08-18
Model tuning follows a predictable cycle — change code, train, compare validation score, decide next step — with human attention required every iteration. The author shows how to automate the loop with an agent:
- The agent proposes changes to the training script;
- A fixed evaluation step checks validation loss;
- Only versions that improve the metric are kept.
The article walks through structuring the loop and ships a companion repo so you can apply the same approach to your own training script.
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