BAAI/AREX-2 - 27B - Agent model based on Qwen3.8 27B
AREX-2 is a 27B-parameter agent model from the Beijing Academy of Artificial Intelligence (BAAI), based on a Qwen3.8-compatible multimodal architecture. It features long-horizon self-improvement, feedback-driven reflection, and cross-domain performance, learning to refine solutions over multiple test-time rounds. Trained on machine-learning and algorithmic-programming tasks with verifiable feedback, AREX-2's self-improvement behavior transfers to deep research, sustaining productive iteration as the task budget grows.
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PublishedOffset at this time: UTC+0Sep 30, 2026, 08:21 UTC
IngestedOffset at this time: UTC+0Sep 30, 2026, 11:00 UTC
- Published
- Sep 30, 2026, 08:21
- Ingested
- Sep 30, 2026, 11:00
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"AREX-2 is a 27B-parameter long-horizon agent model from the Beijing Academy of Artificial Intelligence (BAAI). It learns to improve a solution over multiple test-time rounds: propose, measure, reflect, and revise.
AREX-2 is trained on machine-learning and algorithmic-programming tasks with verifiable feedback, together with the existing AREX deep-research data. The learned self-improvement behavior transfers to deep research without adding new search trajectories.
- Feedback-driven reflection: reads scores, logs, errors, and timings to decide what to change next.
- Cross-domain performance: training on coding and machine-learning tasks also improves the model's deep-research performance.