Summary of Training Agents with Weakly Supervised Feedback From Large Language Models, by Dihong Gong et al.
Training Agents with Weakly Supervised Feedback from Large Language Models
by Dihong Gong, Pu Lu, Zelong Wang, Meng Zhou, Xiuqiang He
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces a novel training method for Large Language Models (LLMs) that enables them to learn complex tasks through iterative environmental interaction. Unlike existing methods, this approach doesn’t require expert-provided trajectories or definitive feedback. Instead, it uses weakly supervised signals from a critic LLM to train the agent in an iterative manner. The agent generates initial trajectories, and then a critic LLM selects good ones, which are used to update the agent, allowing it to generate improved trajectories in subsequent iterations. The method is tested on the API-bank dataset, showing consistent improvement and comparable performance to GPT-4, despite using open-source models with fewer parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a computer program that can learn new things by trying different actions and seeing how they work out. This paper shows how to teach these programs to get better at doing tasks on their own, without needing someone to tell them exactly what to do or showing them the right way to do it. Instead, it uses another program to help decide which actions were good ones, and then uses those good actions to improve itself over time. This approach is tested on a big dataset and shows that it can be just as effective as more advanced methods, even using simpler computer models. |
Keywords
» Artificial intelligence » Gpt » Supervised
