6 citations · 6 across the 6 of their papers we have counts for
9 papers · 1 filter
GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?
Tongxu Luo, Rongsheng Wang, Jiaxi Bi +22
Game generation is an emerging application of coding agents, requiring models to transform natural-language specifications into playable interactive systems. Unlike traditional cod…
Enabling Doctor-Centric Medical AI with LLMs through Workflow-Aligned Tasks and Benchmarks
Wenya Xie, Qingying Xiao, Yu Zheng +8
The rise of large language models (LLMs) has transformed healthcare by offering clinical guidance, yet their direct deployment to patients poses safety risks due to limited domain…
QFFT, Question-Free Fine-Tuning for Adaptive Reasoning
Wanlong Liu, Junxiao Xu, Fei Yu +7
Recent advancements in Long Chain-of-Thought (CoT) reasoning models have improved performance on complex tasks, but they suffer from overthinking, which generates redundant reasoni…
The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models
Ke Ji, Jiahao Xu, Tian Liang +10
Improving the reasoning capabilities of large language models (LLMs) typically requires supervised fine-tuning with labeled data or computationally expensive sampling. We introduce…
RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions
Wanlong Liu, Junying Chen, Ke Ji +3
Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models (LLMs) by incorporating external knowledge. However, current RAG methods face…
HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs
Junying Chen, Zhenyang Cai, Ke Ji +5
The breakthrough of OpenAI o1 highlights the potential of enhancing reasoning to improve LLM. Yet, most research in reasoning has focused on mathematical tasks, leaving domains lik…