# AREX-2

AREX-2 defines agent self-improvement as turning more test-time rounds into a better solution, synthesizes long-horizon improvement trajectories from machine learning engineering and algorithmic programming tasks that retain failures and regressions, and after fine-tuning Qwen3.8-27B reaches 81.8 on MLE-bench Lite and 70.7 on Frontier-CS while lifting BrowseComp to 84.0, HLE to 52.6, GAIA to 92.2 and DeepSearchQA to 93.8 without adding new deep-research data, with continued gains as the round budget grows.

AREX-2 defines agent self-improvement as turning more test-time rounds into a better solution, synthesizes long-horizon improvement trajectories from machine learning engineering and algorithmic programming tasks that retain failures and regressions, and after fine-tuning Qwen3.8-27B reaches 81.8 on MLE-bench Lite and 70.7 on Frontier-CS while lifting BrowseComp to 84.0, HLE to 52.6, GAIA to 92.2 and DeepSearchQA to 93.8 without adding new deep-research data, with continued gains as the round budget grows.