5 papers · 1 filter
WorldDynCache: Risk-Controlled Latent Dynamics Approximation for Diffusion World Model
Leyang Chen, Junyi Wu, Shaoqiu Zhang +1
Diffusion world models generate high-quality futures, but re- peated transformer evaluations make inference prohibitively slow. Existing caches reuse intermediate features, selecti…
Factorized Spectral Representations for Reinforcement Learning
Junyi Wu, Dan Li
Learning a compact model of the world from interaction data is central to sample-efficient deep reinforcement learning. Spectral representation methods have become the leading para…
Elastic-dLLM: Position Preserving Context Compression and Augmentation of Diffusion LLMs
Junyi Wu, Tianchen Zhao, Shaoqiu Zhang +3
Unlike autoregressive models, which generate one token at a time, dLLMs denoise a chunk of [MASK] tokens jointly and sample one or more tokens per step; despite enabling parallel d…
PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structure
Junyi Wu, Guang Lin
We propose PO-CKAN, a physics-informed deep operator framework based on Chunkwise Rational Kolmogorov--Arnold Networks (KANs), for approximating the solution operators of partial d…
Tensor-Efficient High-Dimensional Q-learning
Junyi Wu, Dan Li
High-dimensional reinforcement learning(RL) faces challenges with complex calculations and low sample efficiency in large state-action spaces. Q-learning algorithms struggle partic…