2 citations · 2 across the 7 of their papers we have counts for
8 papers
Value-Guided Repulsion for Offline Reinforcement Learning
Yixue Hao, Jiahao Zhang, Jiesong Lian +5
Drifting models have emerged as a highly efficient and stable paradigm for policy design in offline reinforcement learning (RL). However, standard drift policies determine repulsio…
SARA: Semantically Adaptive Relational Alignment for Video Diffusion Models
Jiesong Lian, Zixiang Zhou, Ruizhe Zhong +6
Recent video diffusion models (VDMs) synthesize visually convincing clips, yet still drop entities, mis-bind attributes, and weaken the interactions specified in the prompt. Repres…
Asymmetric On-Policy Distillation: Bridging Exploitation and Imitation at the Token Level
Nan Jia, Haojin Yang, Xing Ma +6
On-policy distillation (OPD) trains a student on its own trajectories with token-level teacher feedback and often outperforms off-policy distillation and standard reinforcement lea…
Euphonium: Steering Video Flow Matching via Process Reward Gradient Guided Stochastic Dynamics
Ruizhe Zhong, Jiesong Lian, Xiaoyue Mi +4
While online Reinforcement Learning has emerged as a crucial technique for aligning flow matching models with human preferences, current approaches are hindered by inefficient expl…
SoliReward: Mitigating Susceptibility to Reward Hacking and Annotation Noise in Video Generation Reward Models
Jiesong Lian, Ruizhe Zhong, Zixiang Zhou +6
Post-training alignment of video generation models with human preferences is a critical goal. Developing effective Reward Models (RMs) for this process faces significant methodolog…
Video Generation Models Are Good Latent Reward Models
Xiaoyue Mi, Wenqing Yu, Jiesong Lian +9
Reward feedback learning (ReFL) has proven effective for aligning image generation with human preferences. However, its extension to video generation faces significant challenges.…
Conflux-PSRO: Effectively Leveraging Collective Advantages in Policy Space Response Oracles
Yucong Huang, Jiesong Lian, Mingzhi Wang +2
Policy Space Response Oracle (PSRO) with policy population construction has been demonstrated as an effective method for approximating Nash Equilibrium (NE) in zero-sum games. Exis…
Fusion-PSRO: Nash Policy Fusion for Policy Space Response Oracles
Jiesong Lian, Yucong Huang, Chengdong Ma +4
For solving zero-sum games involving non-transitivity, a useful approach is to maintain a policy population to approximate the Nash Equilibrium (NE). Previous studies have shown th…