Evo² trains one model to both rewrite its own agent harness and learn from the edits, hitting 48.3% on SWE-bench Verified

A single policy proposes harness edits (prompts, tools, context, feedback handling) and reflects on their effect, and those runs then train its weights via SFT plus interleaved GRPO, reaching 82.1% on Polyglot and 48.3% on SWE-bench Verified versus 62.6% and 41.0% for the best prior self-evolution method. The harness loop alone lifted a GPT-5-mini agent from 24.4% to 48.3%, and the evolved harnesses transferred to other benchmarks without further tuning.