Summary of A Minimaximalist Approach to Reinforcement Learning From Human Feedback, by Gokul Swamy et al.
A Minimaximalist Approach to Reinforcement Learning from Human Feedback
by Gokul Swamy, Christoph Dann, Rahul Kidambi, Zhiwei Steven Wu, Alekh Agarwal
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper introduces Self-Play Preference Optimization (SPO), an algorithm for reinforcement learning from human feedback. Unlike traditional methods, SPO does not require training a reward model or adversarial training and is simpler to implement. It can handle complex preferences, including non-Markovian, intransitive, and stochastic ones, while being robust to compounding errors. The approach builds upon the Minimax Winner (MW) concept from social choice theory, framing learning as a zero-sum game between two policies. By leveraging this symmetry, SPO uses a single agent playing against itself to compute the MW, achieving strong convergence guarantees. This method outperforms reward-model-based approaches on continuous control tasks, demonstrating efficient and robust learning from human preferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops an innovative way for machines to learn from people’s opinions. The approach is simple and doesn’t require complex training. It can handle situations where people have different or changing preferences, even when these are hard to predict. By having a single machine play against itself, the algorithm ensures it learns efficiently and accurately. This method beats existing approaches in various scenarios where machines need to learn from human feedback. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning from human feedback
