Summary of Reinforcement Learning with Generative Models For Compact Support Sets, by Nico Schiavone and Xingyu Li
Reinforcement Learning with Generative Models for Compact Support Sets
by Nico Schiavone, Xingyu Li
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 The proposed framework utilizes reinforcement learning as a control for foundation models to generate synthetic support sets that can augment the performance of neural network models on real data classification tasks. The framework allows a reinforcement learning agent to access a novel context-based dictionary and use it with a novel prompt structure to form and optimize prompts as inputs to generative models, receiving feedback based on a reward function combining the change in validation accuracy and entropy. The support set is formed through several exploration steps, resulting in excellent performance increases for no additional labeling or data cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses machine learning to help computers learn from lots of information. It’s like helping a student study by giving them focused questions that make them smarter. The researchers created a way to use this idea with special models called “foundation models” that already know a lot about the world. They used a new type of learning called “reinforcement learning” that helps the model find the best questions to ask and learn from its answers. This helped the model create small groups of examples that can help other computers make better predictions. The results were very good, with significant improvements in how well the models worked without needing any more data or labels. |
Keywords
» Artificial intelligence » Classification » Machine learning » Neural network » Prompt » Reinforcement learning
