Chenfeng Xu

REAR transforms how we achieve user preference alignment in LLMs, enabling scalable realignment without costly retraining.

Achieving up to 88x efficiency gains, Taylor-Calibrate transforms the way we initialize hybrid linear attention models, drastically reducing the training burden.

Discrete diffusion policies, typically used for image generation, turn out to be surprisingly effective and efficient asynchronous executors for robots acting in dynamic environments, outperforming traditional continuous control methods.

Forget fancy quantization schemes – a simple token-wise INT4 quantization with Hadamard rotation is all you need to nearly match FP16 accuracy in LLM serving, without sacrificing throughput.

Sparse queries offer a surprisingly effective and efficient alternative to dense representations for image-to-3D generation, achieving comparable fidelity with less input-view bias.

Diffusion language models can now match autoregressive quality, thanks to a clever trick that forces them to agree with themselves.