Summary of The Perils Of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret, by Lukas Fluri et al.
The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret
by Lukas Fluri, Leon Lang, Alessandro Abate, Patrick Forré, David Krueger, Joar Skalse
First submitted to arxiv on: 22 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 explores the challenges in reinforcement learning where specifying a suitable reward function can be difficult. Reward learning aims to overcome this issue by learning the reward function itself. However, even if the learned reward model has a low error on the data distribution, it may still produce a policy with large regret. This phenomenon is known as an “error-regret mismatch.” The main cause of such mismatches is the distributional shift that occurs during policy optimization. The authors mathematically prove that a sufficiently low expected test error of the reward model guarantees low worst-case regret, but also demonstrate that realistic data distributions can still lead to error-regret mismatch for any fixed expected test error. Additionally, they show that similar issues persist even when using policy regularization techniques like RLHF. Overall, the paper aims to stimulate research into improved methods for learning reward models and better ways to measure their quality reliably. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a problem in machine learning where it’s hard to create a good “reward function” that tells an AI what to do. To solve this issue, researchers are trying to learn the reward function itself. But even if they succeed, the AI might still make bad decisions. This is called an “error-regret mismatch.” The main reason for this problem is when the data changes during training. The authors prove that if the learned reward model is good enough, it will work well in real-life situations. However, they also show that this doesn’t always happen and that there are cases where the AI can still make bad decisions even with a good reward function. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Regularization » Reinforcement learning » Rlhf
