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Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling
Fan Feng, Yujia Zheng, Minghao Fu +5
Learning and planning in imagination using world models provides an effective paradigm for training agents for decision-making. However, existing approaches often rely on high-dime…
Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making
Fan Feng, Selena Ge, Minghao Fu +6
Recent work has framed decision-making as a sequence modeling problem using generative models such as diffusion models. Although promising, these approaches often overlook latent f…
Transformer Is Inherently a Causal Learner
Xinyue Wang, Stephen Wang, Biwei Huang
We reveal that transformers trained in an autoregressive manner naturally encode time-delayed causal structures in their learned representations. When predicting future values in m…
Factored Causal Representation Learning for Robust Reward Modeling in RLHF
Yupei Yang, Lin Yang, Wanxi Deng +5
A reliable reward model is essential for aligning large language models with human preferences through reinforcement learning from human feedback. However, standard reward models a…
Boosting Efficiency in Task-Agnostic Exploration through Causal Knowledge
Yupei Yang, Biwei Huang, Shikui Tu +1
The effectiveness of model training heavily relies on the quality of available training resources. However, budget constraints often impose limitations on data collection efforts.…
Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations
Yupei Yang, Biwei Huang, Fan Feng +3
General intelligence requires quick adaption across tasks. While existing reinforcement learning (RL) methods have made progress in generalization, they typically assume only distr…