Before They Can Solve: Predicting Post-Training Coding-Agent Performance from Base Models

In 30 Sekunden

Was passiert ist

Researchers developed methods to predict how well base coding models will perform after post-training as agents, without running expensive training.

Warum es zählt

ML engineers selecting which base models to fine-tune for coding tasks need early signals before committing compute to post-training.

Achtung

Methods rely on having successful post-trained trajectories and verifiers available; results shown on ten model pairs, generalization unclear.