In the news
Before They Can Solve: Predicting Post-Training Coding-Agent Performance from Base Models
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- Researchers developed methods to predict how well base coding models will perform after post-training as agents, without running expensive training.
- Why it matters
- ML engineers selecting which base models to fine-tune for coding tasks need early signals before committing compute to post-training.
- Watch out
- Methods rely on having successful post-trained trajectories and verifiers available; results shown on ten model pairs, generalization unclear.
- agent
- agentic
- prompt
- post-train
The patterns behind this
Each one covers how the technique works, when it earns its cost, and where it breaks.
The Agent Architect
One pattern, one tradeoff, one production failure story. A short weekly briefing for people building agentic systems.
Weekly email, one-click unsubscribe. We only use your address to send the briefing.