AREX-2 AI research develops LLM agents that learn from mistakes over time

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A new research paper titled 'AREX-2' introduces a method to improve autonomous AI agents. It addresses a common limitation where large language models fail at long, complex tasks requiring sustained reasoning and self-correction. The system trains agents using verifiable improvement trajectories from domains like machine learning and programming. These domains provide clear feedback, such as test results, to label genuine improvement. The approach aims to decouple the skill of iterative self-improvement from raw knowledge, allowing performance to scale with more reflection time.

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