1 citations · 1 across the 4 of their papers we have counts for
8 papers
SkillForge: Evolving Verifiable Skills for Reinforcement Learning Agents
Shidong Yang, Ziyu Ma, Tongwen Huang +5
Large language model (LLM) agents are trained with reinforcement learning (RL) for complex decision-making tasks. However, most RL-trained agents remain episodic and cannot accumul…
Role-Agent: Bootstrapping LLM Agents via Dual-Role Evolution
Xucong Wang, Ziyu Ma, Shidong Yang +4
Although Large Language Model (LLM) agents have demonstrated strong performance on complex tasks, their learning is often limited by inefficient interaction feedback and static tra…
Towards High-Resolution Visual Perception via Hierarchical Entity Exploration
Ziyu Ma, Shidong Yang, Yuxiang Ji +5
High-resolution (HR) image perception remains a key challenge in multimodal large language models (MLLMs), as fine-grained details are often lost when the image is processed as a w…
CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution
Shidong Yang, Ziyu Ma, Tongwen Huang +3
Reinforcement learning for LLM agents is typically conducted on a static data distribution, which fails to adapt to the agent's evolving behavior and leads to poor coverage of comp…
SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
Ziyu Ma, Shidong Yang, Yuxiang Ji +5
Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment. As a result, similar w…
Entropy-Guided Data-Efficient Training for Multimodal Reasoning Reward Models
Shidong Yang, Tongwen Huang, Hao Wen +3
Multimodal reward models are crucial for aligning multimodal large language models with human preferences. Recent works have incorporated reasoning capabilities into these models,…