2 citations · 2 across the 3 of their papers we have counts for
5 papers
Pareto-Optimal RTL Code Generation via Multi-Objective Reinforcement Learning with Large Language Models
Koki Takeshita, Kenji Uebayashi, Hikaru Tomonari +6
Recent studies have proposed reinforcement learning with verifiable rewards (RLVR) methods that enable LLMs to generate high-quality RTL circuits from natural-language specificatio…
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…
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…
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…
Hitachi at SemEval-2023 Task 3: Exploring Cross-lingual Multi-task Strategies for Genre and Framing Detection in Online News
Yuta Koreeda, Ken-ichi Yokote, Hiroaki Ozaki +3
This paper explains the participation of team Hitachi to SemEval-2023 Task 3 "Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setu…