Brief

Researchers have developed SERA, a method for efficiently training coding agents specialized for private codebases. This approach uses Soft Verified Generation to create synthetic training data without requiring unit tests, making it significantly cheaper than previous methods. SERA achieves leading performance among open-source models and matches some open-weight models, with the potential to accelerate research in adaptable coding agents. AI