3 papers
cs.CL2026
Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks
Masaya Tsunokake, Yuta Koreeda, Terufumi Morishita +3
When applying LLMs to real-world enterprise operations, LLMs need to handle proprietary knowledge in small domains of specific operations (). A previous stu…
cs.CL2025
Agent Fine-tuning through Distillation for Domain-specific LLMs in Microdomains
Yawen Xue, Masaya Tsunokake, Yuta Koreeda +3
Agentic large language models (LLMs) have become prominent for autonomously interacting with external environments and performing multi-step reasoning tasks. Most approaches levera…
cs.LG2024
GFlowNet Fine-tuning for Diverse Correct Solutions in Mathematical Reasoning Tasks
Ryoichi Takase, Masaya Tsunokake, Yuta Tsuchiya +1
Mathematical reasoning problems are among the most challenging, as they typically require an understanding of fundamental laws to solve. The laws are universal, but the derivation…