6 papers
CPMobius: Iterative Coach-Player Reasoning for Data-Free Reinforcement Learning
Ran Li, Zeyuan Liu, Yinghao Chen +8
Large Language Models (LLMs) have demonstrated strong potential in complex reasoning, yet their progress remains fundamentally constrained by reliance on massive high-quality human…
FlowEvo: Self-Evolving Agents through the Co-Evolution of Workflows and Executable Skills
Zeyu Ren, Ling Yue, Ran Li +5
Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episode are usually discarded after execution.…
How Far Can Unsupervised RLVR Scale LLM Training?
Bingxiang He, Yuxin Zuo, Zeyuan Liu +18
Unsupervised reinforcement learning with verifiable rewards (URLVR) offers a pathway to scale LLM training beyond the supervision bottleneck by deriving rewards without ground trut…
Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models
Xin Li, Weize Chen, Qizhi Chu +9
The need to analyze graphs is ubiquitous across various fields, from social networks to biological research and recommendation systems. Therefore, enabling the ability of large lan…
Co-Saving: Resource Aware Multi-Agent Collaboration for Software Development
Rennai Qiu, Chen Qian, Ran Li +9
Recent advancements in Large Language Models (LLMs) and autonomous agents have demonstrated remarkable capabilities across various domains. However, standalone agents frequently en…
EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents
Zhili Cheng, Yuge Tu, Ran Li +9
Multimodal Large Language Models (MLLMs) have shown significant advancements, providing a promising future for embodied agents. Existing benchmarks for evaluating MLLMs primarily u…