3 papers
cs.LG2026
Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining
Jie Cheng, Ruixi Qiao, Yingwei Ma +5
A significant aspiration of offline reinforcement learning (RL) is to develop a generalist agent with high capabilities from large and heterogeneous datasets. However, prior approa…
cs.AI2025
Stop Summation: Min-Form Credit Assignment Is All Process Reward Model Needs for Reasoning
Jie Cheng, Gang Xiong, Ruixi Qiao +5
Process reward models (PRMs) have proven effective for test-time scaling of Large Language Models (LLMs) on challenging reasoning tasks. However, reward hacking issues with PRMs li…
cs.LG2025
Offline Reinforcement Learning with Discrete Diffusion Skills
RuiXi Qiao, Jie Cheng, Xingyuan Dai +2
Skills have been introduced to offline reinforcement learning (RL) as temporal abstractions to tackle complex, long-horizon tasks, promoting consistent behavior and enabling meanin…