Summary of Reinforcement Learning Without Human Feedback For Last Mile Fine-tuning Of Large Language Models, by Alec Solway
Reinforcement Learning without Human Feedback for Last Mile Fine-Tuning of Large Language Models
by Alec Solway
First submitted to arxiv on: 29 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Reinforcement learning is used to align language models with human preference signals after pre-training on a large corpus using likelihood maximization. Typically, models are fine-tuned on task-specific data before deployment. However, this paper develops a framework for last-mile fine-tuning using reinforcement learning, which has advantages over likelihood maximization. Unlike imitation learning, reinforcement learning trains a model to explore the policy space and suppress poor actions. The framework is tested on abstractive summarization tasks, yielding significantly better results than likelihood maximization. While post-processing can bridge some gaps, this framework offers a new avenue for optimizing models in complex situations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a new way to fine-tune language models so they match what humans like. First, the model is trained on a lot of text using a common method called likelihood maximization. Then, it’s fine-tuned again to get better results. The new way, called reinforcement learning, helps the model learn by doing things and getting rewards or penalties. This approach was tested on summarizing long pieces of text and worked much better than the usual method. It’s a useful tool for making language models more accurate and understanding human preferences. |
Keywords
» Artificial intelligence » Fine tuning » Likelihood » Reinforcement learning » Summarization
