Search Results for author:
Found 8 papers, 1 papers with code
CombatVLA: An Efficient Vision-Language-Action Model for Combat Tasks in 3D Action Role-Playing Games
Recent advances in Vision-Language-Action models (VLAs) have expanded the capabilities of embodied intelligence.
Adaptive Dense Reward: Understanding the Gap Between Action and Reward Space in Alignment
Our method improves the success rate on adversarial samples by 10\% compared to the sample-wise approach, and achieves a 1. 3\% improvement on evaluation benchmarks such as MMLU, GSM8K, HumanEval, etc.
DDK: Distilling Domain Knowledge for Efficient Large Language Models
Despite the advanced intelligence abilities of large language models (LLMs) in various applications, they still face significant computational and storage demands.
Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach
By leveraging this feature, C4 can rapidly identify the faulty components, swiftly isolate the anomaly, and restart the task, thereby avoiding resource wastage caused by delays in anomaly detection.
D-CPT Law: Domain-specific Continual Pre-Training Scaling Law for Large Language Models
To address the limitations of existing methods, inspired by the Scaling Law for performance prediction, we propose to investigate the Scaling Law of the Domain-specific Continual Pre-Training (D-CPT Law) to decide the optimal mixture ratio with acceptable training costs for LLMs of different sizes.
PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems
However, we observe that GPU devices in training recommender systems are underutilized, and they cannot attain an expected throughput improvement as what it has achieved in CV and NLP areas.
M6-T: Exploring Sparse Expert Models and Beyond
Mixture-of-Experts (MoE) models can achieve promising results with outrageous large amount of parameters but constant computation cost, and thus it has become a trend in model scaling.
Understanding Chinese Video and Language via Contrastive Multimodal Pre-Training
The pre-trained neural models have recently achieved impressive performances in understanding multimodal content.
