9 accepted papers
Since next-scale prediction was introduced as a new paradigm for autoregressive image generation, it has attracted extensive research interest. By progressively increasing resolution in a draft-to-refinement process, next-scale prediction demonstrates great potential in both generation quality and e
Diffusion policies have recently been as a powerful paradigm for visuomotor control in robotic manipulation due to their ability to model the distribution of action sequences and capture multimodality. However, iterative denoising leads to substantial inference latency, limiting control frequency in…
Circuit representation learning has become pivotal in electronic design automation, enabling critical tasks such as testability analysis, logic reasoning, power estimation, and SAT solving. However, existing models face significant challenges in scaling to large circuits due to limitations like over…
Training Long-Context Large Language Models (LLMs) is challenging, as hybrid training with long-context and short-context data often leads to workload imbalances. Existing works mainly use data packing to alleviate this issue, but fail to consider imbalanced attention computation and wasted communic…
Vision-language instruction-tuning models have recently achieved significant performance improvements. In this work, we discover that large-scale 3D parallel training on those models leads to an imbalanced computation load across different devices. The vision and language parts are inherently hetero…
The application of vision-language models (VLMs) has achieved impressive success in various robotics tasks. However, there are few explorations for foundation models used in quadruped robot navigation through terrains in 3D environments. We introduce SARO (Space-Aware Robot System for Terrain Crossi
Large language models (LLMs) show great performance in various tasks, but face deployment challenges from limited memory capacity and bandwidth.Low-bit weight quantization can save memory and accelerate inference.Although floating-point (FP) formats show good performance in LLM quantization, they te…
The upscaling of Large Language Models (LLMs) has yielded impressive advances in natural language processing, yet it also poses significant deployment challenges. Weight quantization has emerged as a widely embraced solution to reduce memory and computational demands. This paper introduces BitDistil…
"In autonomous driving, there is growing interest in end-to-end online vectorized map perception in bird’s-eye-view (BEV) space, with an expectation that it could replace traditional high-cost offline high-definition (HD) maps. However, the accuracy and robustness of these methods can be easily comp…