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  "text": "Designing RL curricula for robots is tedious and brittle.\n\nBut what if LLMs could design the entire curriculum from a natural language prompt?\n\nThis new research introduces AURA, a framework where specialized LLM agents autonomously design multi-stage RL curricula. You describe the task in plain English, and AURA generates complete YAML specifications for rewards, domain randomization, and training configs.\n\nThree key ideas make this work:\n\n- First, a typed YAML schema validates all outputs before any GPU cycles are spent, catching errors through static checks rather than failed training runs.\n\n- Second, a multi-agent architecture decomposes the problem: a high-level planner designs the curriculum structure while stage-level agents handle the details.\n\n- Third, a retrieval-augmented feedback loop stores prior curricula and outcomes in a vector database, letting agents learn from experience across tasks.\n\nAURA achieves a 99% training-launch success rate. Without schema validation, success drops to 47%. Without the vector database, it falls to 38%. A single-agent setup manages only 7%. By comparison, CurricuLLM achieves 31%, and Eureka reaches 12-49% depending on task complexity.\n\nPaper: https://t.co/1ZYoi1DbzH\n\nLearn to build effective AI agents in our academy: https://t.co/zQXQt0PMbG",
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