Summary of Supervised Fine-tuning As Inverse Reinforcement Learning, by Hao Sun
Supervised Fine-Tuning as Inverse Reinforcement Learning
by Hao Sun
First submitted to arxiv on: 18 Mar 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 The proposed work challenges the traditional approach to aligning Large Language Models (LLMs) by questioning the effectiveness of preference datasets and exploring alternative methods using expert demonstrations. A sequential decision-making framework is developed to formulate the problem of LLM alignment, drawing insights from inverse reinforcement learning and imitation learning. Various approaches are introduced for divergence minimization in LLM alignment tasks, highlighting mass-covering and mode-seeking behaviors. The pros and cons of classical supervised fine-tuning are also examined. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) need to be aligned with human preferences, but this usually requires specific datasets or feedback. Researchers are now exploring a new approach that uses expert demonstrations instead. A special framework is created to solve this problem, combining ideas from other areas of machine learning. The results show different methods for minimizing errors in LLM alignment, and how these methods behave. This research helps us understand the strengths and weaknesses of traditional fine-tuning techniques. |
Keywords
* Artificial intelligence * Alignment * Fine tuning * Machine learning * Reinforcement learning * Supervised
