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Taro Yano

2 accepted papers

2025

Can Large Language Models Invent Algorithms to Improve Themselves?

NAACL 2025long

Large Language Models (LLMs) have shown remarkable performance improvements and are rapidly gaining adoption in industry. However, the methods for improving LLMs are still designed by humans, which restricts the invention of new model-improving algorithms to human expertise and imagination. To addre…

2025

LaMDAgent: An Autonomous Framework for Post-Training Pipeline Optimization via LLM Agents

EMNLP 2025

Large Language Models (LLMs) excel across diverse tasks, with post-training methods like Supervised Fine-Tuning (SFT), Preference Learning, and Model Merging enabling effective domain and task adaptation. While outcomes can vary with data orderings or component combinations, yet manual pipeline opti

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