0.6B Model Picks Coding Agent Fixes: 21% Fewer Attempts, 12% Fewer Tokens
orlitany · x · 2026-10-22
Most coding agents decide which fix to try next based on test pass rate, but passing more tests doesn't mean you're closer to a fix. A new approach trains a 0.6B model to read the test failure and each proposed fix, then pick the most promising one — cutting attempts by 21% and token usage by 12%.
More from coding & agent
- Dev builds a cookie clicker for humans and AI on ChatGPT Sites with database and MCP — craigsdennis · 2026-10-22
- Dev builds a human-and-AI Cookie Clicker with ChatGPT Sites, a database and an MCP server — craigsdennis · 2026-10-22
- Start agent factory automation: one idle issue per day, iterate on quality — intellectronica · 2026-10-22
- Recursive releases Flint: 40B MoE local coding model, built in 4 months — _rockt · 2026-10-22
- Hamel Husain and Shreya Shankar Launch 'Evals for AI Engineers' Book Nov 12 — HamelHusain · 2026-10-22
- EAU meta-agent automates RL training: 36.7% higher task completion on ColBench — xiye_nlp · 2026-10-22