3 citations · 4 across the 8 of their papers we have counts for
8 papers
Kernel-Smith: A Unified Recipe for Evolutionary Kernel Optimization
He Du, Qiming Ge, Jiakai Hu +18
We present Kernel-Smith, a framework for high-performance GPU kernel and operator generation that combines a stable evaluation-driven evolutionary agent with an evolution-oriented…
TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration
Zerun Ma, Guoqiang Wang, Xinchen Xie +7
While Large Language Models (LLMs) have empowered AI research agents to perform isolated scientific tasks, automating complex, real-world workflows, such as LLM training, remains a…
How to Fine-Tune a Reasoning Model? A Teacher-Student Cooperation Framework to Synthesize Student-Consistent SFT Data
Zixian Huang, Kaichen Yang, Xu Huang +6
A widely adopted strategy for model enhancement is to use synthetic data generated by a stronger model for supervised fine-tuning (SFT). However, for emerging reasoning models like…
EvoSyn: Generalizable Evolutionary Data Synthesis for Verifiable Learning
He Du, Bowen Li, Aijun Yang +3
Reliable verifiable data has become a key driver of capability gains in modern language models, enabling stable reinforcement learning with verifiable rewards and effective distill…
Confidence as a Reward: Transforming LLMs into Reward Models
He Du, Bowen Li, Chengxing Xie +3
Reward models can significantly enhance the reasoning capabilities of large language models (LLMs), but they typically require extensive curated data and costly training. To mitiga…
Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation
Chengwen Qi, Ren Ma, Bowen Li +5
First-order logic (FOL) reasoning, which involves sequential deduction, is pivotal for intelligent systems and serves as a valuable task for evaluating reasoning capabilities, part…
SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution
Chengxing Xie, Bowen Li, Chang Gao +4
Large Language Models (LLMs) have demonstrated remarkable proficiency across a variety of complex tasks. One significant application of LLMs is in tackling software engineering cha…
Prompting Large Language Models to Tackle the Full Software Development Lifecycle: A Case Study
Bowen Li, Wenhan Wu, Ziwei Tang +13
Recent advancements in large language models (LLMs) have significantly enhanced their coding capabilities. However, existing benchmarks predominantly focused on simplified or isola…