4 citations · 10 across the 10 of their papers we have counts for
11 papers
Rep2Skill: Representation-Guided Skill Self-Evolution for LLM Agents
Kaixing Zhang, Changming Li, Yingdong Shi +5
Textual skills enable large language model (LLM) based agents to accumulate reusable procedural knowledge without updating model parameters. Yet existing skill evolution remains la…
Dynamic Rollout Editing for Reducing Overthinking in RL-Trained Reasoning Models
Zihao Wei, Wenjie Shi, Liang Pang +8
Long-form chain-of-thought reasoning can improve LLM performance on complex tasks, but models often continue generating unnecessary reasoning after a correct answer has emerged. We…
Beyond Reasoning: Reinforcement Learning Unlocks Parametric Knowledge in LLMs
Wanli Yang, Hongyu Zang, Junwei Zhang +5
Reinforcement learning (RL) has achieved remarkable success in LLM reasoning, but whether it can also improve direct recall of parametric knowledge remains an open question. We stu…
HeavySkill: Heavy Thinking as the Inner Skill in Agentic Harness
Jianing Wang, Linsen Guo, Zhengyu Chen +8
Recent advances in agentic harness with orchestration frameworks that coordinate multiple agents with memory, skills, and tool use have achieved remarkable success in complex reaso…
DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
Tianhao Hu, Xiangcheng Liu, Yuchun Miao +16
Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time -- is bottlenecked by skewed ge…
Leveraging Reward Consistency for Interpretable Feature Discovery in Reinforcement Learning
Qisen Yang, Huanqian Wang, Mukun Tong +3
The black-box nature of deep reinforcement learning (RL) hinders them from real-world applications. Therefore, interpreting and explaining RL agents have been active research topic…
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning
Wenjie Shi, Gao Huang, Shiji Song +1
Deep reinforcement learning (RL) agents are becoming increasingly proficient in a range of complex control tasks. However, the agent's behavior is usually difficult to interpret du…
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning
Wenjie Shi, Gao Huang, Shiji Song +3
Deep reinforcement learning (RL) has recently led to many breakthroughs on a range of complex control tasks. However, the agent's decision-making process is generally not transpare…
Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles
Wenjie Shi, Shiji Song, Cheng Wu +1
This paper investigates trajectory tracking problem for a class of underactuated autonomous underwater vehicles (AUVs) with unknown dynamics and constrained inputs. Different from…
Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learning
Wenjie Shi, Shiji Song, Cheng Wu
Maximum entropy deep reinforcement learning (RL) methods have been demonstrated on a range of challenging continuous tasks. However, existing methods either suffer from severe inst…
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning
Wenjie Shi, Shiji Song, Hui Wu +3
Model-free deep reinforcement learning (RL) algorithms have been widely used for a range of complex control tasks. However, slow convergence and sample inefficiency remain challeng…