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cs.CL2024
Can Large Language Models Invent Algorithms to Improve Themselves?: Algorithm Discovery for Recursive Self-Improvement through Reinforcement Learning
Yoichi Ishibashi, Taro Yano, Masafumi Oyamada
Large Language Models (LLMs) have achieved remarkable capabilities, yet their improvement methods remain fundamentally constrained by human design. We present Self-Developing, a fr…
cs.SE2024★ 6 cited
Self-Organized Agents: A LLM Multi-Agent Framework toward Ultra Large-Scale Code Generation and Optimization
Yoichi Ishibashi, Yoshimasa Nishimura
Recent advancements in automatic code generation using large language model (LLM) agent have brought us closer to the future of automated software development. However, existing si…