Training Agent Harness Like Training a ML Model
SMRTR summary
A developer built a PyTorch-inspired framework that trains AI agent "harnesses" — the systems controlling what a model sees and does — similarly to how machine learning models are trained, using iterative experiments with a gradient-like improvement loop. Applied to coding benchmarks, the framework improved task solve rates from 8/39 to 14/39 on SWE-Bench and 16/38 to 23/38 on Terminal Bench, with trained harnesses boosting performance across multiple different AI models.
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