5 papers
ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies
Haodi Hu, Chung-Ta Huang, Jing Liu +4
Vision-language-action (VLA) policies provide strong priors for language-conditioned manipulation, but remain brittle in off-nominal states requiring targeted recovery. We propose…
Embedding Morphology into Transformers for Cross-Robot Policy Learning
Kei Suzuki, Jing Liu, Ye Wang +4
Cross-robot policy learning -- training a single policy to perform well across multiple embodiments -- remains a central challenge in robot learning. Transformer-based policies, su…
AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent
Jing Liu, Toshiaki Koike-Akino, Ye Wang +2
To address the enormous size of Large Language Models (LLMs), model compression methods, such as quantization and pruning, are often deployed, especially on edge devices. In this w…
TuneComp: Joint Fine-tuning and Compression for Large Foundation Models
Xiangyu Chen, Jing Liu, Ye Wang +4
To reduce model size during post-training, compression methods, including knowledge distillation, low-rank approximation, and pruning, are often applied after fine-tuning the model…
LatentLLM: Attention-Aware Joint Tensor Compression
Toshiaki Koike-Akino, Xiangyu Chen, Jing Liu +4
Modern foundation models such as large language models (LLMs) and large multi-modal models (LMMs) require a massive amount of computational and memory resources. We propose a new f…