19 citations · 29 across the 17 of their papers we have counts for
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cs.AI2026
Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning
Xuan Zhang, Zhijian Zhou, Lingfeng Qiao +6
Large language model (LLM) agents have demonstrated strong capability in sequential decision-making, yet they remains fundamentally reactive in long-horizon tasks. Unlike humans wh…
cs.AI2025
Count Counts: Motivating Exploration in LLM Reasoning with Count-based Intrinsic Rewards
Xuan Zhang, Ruixiao Li, Zhijian Zhou +7
Reinforcement Learning (RL) has become a compelling way to strengthen the multi step reasoning ability of Large Language Models (LLMs). However, prevalent RL paradigms still lean o…
cs.AI2024★ 4 cited
Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Bootstrapped Prolog-based Chain-of-Thought
Xiaoyu Tan, Yongxin Deng, Xihe Qiu +7
Large language models have demonstrated remarkable capabilities as general-purpose assistants, excelling in a wide range of reasoning tasks and supporting various aspects of daily…