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cs.AI2026
CM2: Reinforcement Learning with Checklist Rewards for Multi-Turn and Multi-Step Agentic Tool Use
Zhen Zhang, Kaiqiang Song, Xun Wang +11
AI agents are increasingly used to solve real-world tasks by reasoning over multi-turn user interactions and invoking external tools. However, applying reinforcement learning to su…
cs.AI2025
Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training
Tianqing Fang, Zhisong Zhang, Xiaoyang Wang +16
General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence, enabling complex reasoning, web interaction, coding, an…
cs.AI2025
Enter the Void - Planning to Seek Entropy When Reward is Scarce
Ashish Sundar, Chunbo Luo, Xiaoyang Wang
Model-based reinforcement learning (MBRL) offers an intuitive way to increase the sample efficiency of model-free RL methods by simultaneously training a world model that learns to…