1 citations · 1 across the 9 of their papers we have counts for
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SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution
Xiachong Feng, Yi Jiang, Xiaocheng Feng +9
Social intelligence, the ability to navigate complex interpersonal interactions, presents a fundamental challenge for language agents. Training such agents via reinforcement learni…
Stratagem: Learning Transferable Reasoning via Trajectory-Modulated Game Self-Play
Xiachong Feng, Deyi Yin, Xiaocheng Feng +9
Games offer a compelling paradigm for developing general reasoning capabilities in language models, as they naturally demand strategic planning, probabilistic inference, and adapti…
Exploring Reasoning Reward Model for Agents
Kaixuan Fan, Kaituo Feng, Manyuan Zhang +7
Agentic Reinforcement Learning (Agentic RL) has achieved notable success in enabling agents to perform complex reasoning and tool use. However, most methods still relies on sparse…
QuadSentinel: Sequent Safety for Machine-Checkable Control in Multi-agent Systems
Yiliu Yang, Yilei Jiang, Qunzhong Wang +5
Safety risks arise as large language model-based agents solve complex tasks with tools, multi-step plans, and inter-agent messages. However, deployer-written policies in natural la…
MME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs
Jiakang Yuan, Tianshuo Peng, Yilei Jiang +8
Logical reasoning is a fundamental aspect of human intelligence and an essential capability for multimodal large language models (MLLMs). Despite the significant advancement in mul…
Equilibrate RLHF: Towards Balancing Helpfulness-Safety Trade-off in Large Language Models
Yingshui Tan, Yilei Jiang, Yanshi Li +6
Fine-tuning large language models (LLMs) based on human preferences, commonly achieved through reinforcement learning from human feedback (RLHF), has been effective in improving th…