149 citations · 219 across the 20 of their papers we have counts for
7 papers · 1 filter
Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes
Yuqian Fu, Haohuan Huang, Kaiwen Jiang +4
On-policy distillation (OPD) is increasingly used in LLM post-training because it can leverage a teacher model to provide dense supervision on student rollouts. The standard implem…
Equilibrium Policy Generalization: A Reinforcement Learning Framework for Cross-Graph Zero-Shot Generalization in Pursuit-Evasion Games
Runyu Lu, Peng Zhang, Ruochuan Shi +5
Equilibrium learning in adversarial games is an important topic widely examined in the fields of game theory and reinforcement learning (RL). Pursuit-evasion game (PEG), as an impo…
ARAC: Adaptive Regularized Multi-Agent Soft Actor-Critic in Graph-Structured Adversarial Games
Ruochuan Shi, Runyu Lu, Yuanheng Zhu +1
In graph-structured multi-agent reinforcement learning (MARL) adversarial tasks such as pursuit and confrontation, agents must coordinate under highly dynamic interactions, where s…
R2PS: Worst-Case Robust Real-Time Pursuit Strategies under Partial Observability
Runyu Lu, Ruochuan Shi, Yuanheng Zhu +1
Computing worst-case robust strategies in pursuit-evasion games (PEGs) is time-consuming, especially when real-world factors like partial observability are considered. While import…
RLAE: Reinforcement Learning-Assisted Ensemble for LLMs
Yuqian Fu, Yuanheng Zhu, Jiajun Chai +4
Ensembling large language models (LLMs) can effectively combine diverse strengths of different models, offering a promising approach to enhance performance across various tasks. Ho…
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement
Zhi Wang, Li Zhang, Wenhao Wu +3
A longstanding goal of artificial general intelligence is highly capable generalists that can learn from diverse experiences and generalize to unseen tasks. The language and vision…
Discretizing Continuous Action Space with Unimodal Probability Distributions for On-Policy Reinforcement Learning
Yuanyang Zhu, Zhi Wang, Yuanheng Zhu +2
For on-policy reinforcement learning, discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without consid…