9 citations · 9 across the 4 of their papers we have counts for
4 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…
JustRL: Scaling a 1.5B LLM with a Simple RL Recipe
Bingxiang He, Zekai Qu, Zeyuan Liu +9
Recent advances in reinforcement learning for large language models have converged on increasing complexity: multi-stage training pipelines, dynamic hyperparameter schedules, and c…
OS-MAP: How Far Can Computer-Using Agents Go in Breadth and Depth?
Xuetian Chen, Yinghao Chen, Xinfeng Yuan +12
Computer-using agents have shown strong potential to boost human productivity and enable new application forms across platforms. While recent advances have led to usable applicatio…
Towards Optimizing with Large Language Models
Pei-Fu Guo, Ying-Hsuan Chen, Yun-Da Tsai +1
In this work, we conduct an assessment of the optimization capabilities of LLMs across various tasks and data sizes. Each of these tasks corresponds to unique optimization domains,…