6 papers
Efficient Tail-Aware Generative Optimization via Flow Model Fine-Tuning
Zifan Wang, Riccardo De Santi, Xiaoyu Mo +3
Fine-tuning pre-trained diffusion and flow models to optimize downstream utilities is central to real-world deployment. Existing entropy-regularized methods primarily maximize expe…
Adaptive Milestone Reward for GUI Agents
Congmin Zheng, Xiaoyun Mo, Xinbei Ma +10
Reinforcement Learning (RL) has emerged as a mainstream paradigm for training Mobile GUI Agents, yet it struggles with the temporal credit assignment problem inherent in long-horiz…
ColorAgent: Building A Robust, Personalized, and Interactive OS Agent
Ning Li, Qiqiang Lin, Zheng Wu +19
With the advancements in hardware, software, and large language model technologies, the interaction between humans and operating systems has evolved from the command-line interface…
HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Device Scenarios
Jun Wang, Jiamu Zhou, Muning Wen +7
Evaluating the performance of LLMs in multi-turn human-agent interactions presents significant challenges, particularly due to the complexity and variability of user behavior. In t…
P3: A Policy-Driven, Pace-Adaptive, and Diversity-Promoted Framework for data pruning in LLM Training
Yingxuan Yang, Huayi Wang, Muning Wen +4
In the rapidly advancing field of Large Language Models (LLMs), effectively leveraging existing datasets during fine-tuning to maximize the model's potential is of paramount import…
Hammer: Robust Function-Calling for On-Device Language Models via Function Masking
Qiqiang Lin, Muning Wen, Qiuying Peng +8
Large language models have demonstrated impressive value in performing as autonomous agents when equipped with external tools and API calls. Nonetheless, effectively harnessing the…