Summary of Policy Improvement Using Language Feedback Models, by Victor Zhong et al.
Policy Improvement using Language Feedback Models
by Victor Zhong, Dipendra Misra, Xingdi Yuan, Marc-Alexandre Côté
First submitted to arxiv on: 12 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Language Feedback Models (LFMs) that identify desirable behavior in instruction following tasks. By leveraging Large Language Models (LLMs) as experts, LFMs improve task-completion rates on three distinct environments by up to 12%. The models also generalize well to unseen environments and can provide human-interpretable feedback without performance loss. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes robots better at following instructions! Researchers created a new way for computers to learn from examples, called Language Feedback Models (LFMs). They tested these models on different tasks and showed that they work really well. The models even get better when they try something new. This is important because it could help us teach robots how to do things more efficiently. |
