New framework uses LLMs to generate code for AI agent training

Researchers have developed a new framework called Dreaming in Code (DiCode) to improve open-ended learning for AI agents. This unsupervised environment design framework uses large language models to generate executable code for environments, creating a curriculum that guides agents toward increasing competence. When tested on the Craftax benchmark, DiCode enabled agents to acquire long-horizon skills, resulting in a 17% improvement in mean return over baseline methods and success on challenging combat tasks. AI

IMPACT This framework could enable more effective training of AI agents in complex, open-ended environments by providing a structured curriculum.

RANK_REASON This is a research paper detailing a new framework and its empirical results on a benchmark. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• Craftax
• DagsHub
• Dreaming in Code
• Gotit.pub
• Hugging Face
• IArxiv
• Konstantinos Mitsides
• ScienceCast

• paper
• model release

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