Summary of Fine-tuning Large Vision-language Models As Decision-making Agents Via Reinforcement Learning, by Yuexiang Zhai et al.
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning
by Yuexiang Zhai, Hao Bai, Zipeng Lin, Jiayi Pan, Shengbang Tong, Yifei Zhou, Alane Suhr, Saining Xie, Yann LeCun, Yi Ma, Sergey Levine
First submitted to arxiv on: 16 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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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 research proposes an algorithmic framework that fine-tunes large vision-language models (VLMs) using reinforcement learning (RL) to efficiently learn optimal decision-making agents in multi-step goal-directed tasks from interactive environments. The framework prompts VLMs to generate chain-of-thought (CoT) reasoning, which enables the model to explore intermediate reasoning steps leading to text-based actions. The output is parsed into executable actions and used to interact with the environment, obtaining task rewards that fine-tune the entire VLM with RL. The proposed method enhances the decision-making capabilities of VLM agents across various tasks, outperforming commercial models like GPT4-V or Gemini. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps computers get better at making decisions by teaching them how to think through problems step-by-step. Usually, these computers are trained on specific instructions and can complete simple tasks, but they struggle with more complex ones that require multiple steps. To fix this, the researchers developed a new way of training these computers using reinforcement learning. This method lets the computer explore different ways of thinking about a problem and then choose the best approach. The results show that this new method is very effective, allowing even large and powerful computers to make better decisions than before. |
Keywords
» Artificial intelligence » Gemini » Reinforcement learning
