Chongxuan Li

MoE dLLMs can outperform leading models with significantly fewer training tokens, challenging assumptions about data efficiency in large-scale language models.

iLLaDA's fully bidirectional diffusion training outperforms traditional autoregressive models, achieving remarkable gains across key language benchmarks.

Transforming any 2D representation into a symmetric form without losing continuity could revolutionize design processes across various fields.