Summary of Clsp: High-fidelity Contrastive Language-state Pre-training For Agent State Representation, by Fuxian Huang et al.
CLSP: High-Fidelity Contrastive Language-State Pre-training for Agent State Representation
by Fuxian Huang, Qi Zhang, Shaopeng Zhai, Jie Wang, Tianyi Zhang, Haoran Zhang, Ming Zhou, Yu Liu, Yu Qiao
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to multimodal learning called High-Fidelity Contrastive Language-State Pre-training (CLSP). The goal is to accurately encode state information into general representations for use with reinforcement learning and multimodal large language models. To achieve this, the authors design a pre-training task that trains an encoder with coarse-grained information, followed by a contrastive learning approach to fine-tune the representation. Additionally, they employ Random Fourier Features (RFF) to enhance numerical information mapping. The paper demonstrates the effectiveness of CLSP in various tasks such as text-state retrieval, reinforcement learning navigation, and multimodal large language model understanding. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can learn from different types of data like pictures, videos, and words. To do this, we need to teach the computer to recognize the importance of information that changes over time, like a game’s state. The researchers created a new way to train the computer by teaching it to classify coarse-grained information first, then fine-tuning its understanding with more precise data. They tested their method and found it works well in tasks like finding specific text or navigating through games. |
Keywords
» Artificial intelligence » Encoder » Fine tuning » Large language model » Reinforcement learning
