4 papers · 1 filter
On the Direction of RLVR Updates for LLM Reasoning: Identification and Exploitation
Kexin Huang, Haoming Meng, Junkang Wu +10
Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models. While existing analyses identify that RLVR-ind…
AgentEvolver: Towards Efficient Self-Evolving Agent System
Yunpeng Zhai, Shuchang Tao, Cheng Chen +10
Autonomous agents powered by large language models (LLMs) have the potential to significantly enhance human productivity by reasoning, using tools, and executing complex tasks in d…
Larger or Smaller Reward Margins to Select Preferences for Alignment?
Kexin Huang, Junkang Wu, Ziqian Chen +6
Preference learning is critical for aligning large language models (LLMs) with human values, with the quality of preference datasets playing a crucial role in this process. While e…
An Auction-based Marketplace for Model Trading in Federated Learning
Yue Cui, Liuyi Yao, Yaliang Li +3
Federated learning (FL) is increasingly recognized for its efficacy in training models using locally distributed data. However, the proper valuation of shared data in this collabor…