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2025-12-14 14:41:08
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Eureka | Human-Level Reward Design via Coding Large Language Models Eureka: Human-Level Reward Design via Coding Large Language Models Jason Ma 1 2 , William Liang 2 , Guanzhi Wang 1 3 , De-An Huang 1 , Osbert Bastani 2 , Dinesh Jayaraman 2 , Yuke Zhu 1 4 , Linxi "Jim" Fan 1‡ , Anima Anandkumar 1 3‡ 1 NVIDIA; 2 UPenn; 3 Caltech; 4 UT Austin; ‡ Equal Advising Corresponding authors: jasonyma@seas.upenn.edu, dr.jimfan.ai@gmail.com arXiv PDF Code Abstract Large Language Models (LLMs) have excelled as high-level semantic planners for sequential decision-making tasks. However, harnessing them to learn complex low-level manipulation tasks, such as dexterous pen spinning, remains an open problem. We bridge this fundamental gap and present ...
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