Summary of Easy-to-hard Generalization: Scalable Alignment Beyond Human Supervision, by Zhiqing Sun et al.
Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision
by Zhiqing Sun, Longhui Yu, Yikang Shen, Weiyang Liu, Yiming Yang, Sean Welleck, Chuang Gan
First submitted to arxiv on: 14 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 This paper addresses the challenge of improving AI systems whose capabilities have surpassed those of humans. Current methods rely on human demonstrations or judgments, which limits the potential of AI. The authors propose an innovative approach to tackle hard reasoning tasks by learning from human annotations on easier tasks, a process they call easy-to-hard generalization. They develop a novel method for scalable alignment that trains reward models on easy problems and uses them to evaluate policy models on harder tasks. The results show that this approach enables AI systems to generalize beyond the level of human supervision. Specifically, their process-supervised reinforcement learning model achieved an accuracy of 34.0% and 52.5% on MATH500, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how AI can learn from humans even when it’s better than them at certain tasks. Right now, we rely on human supervision to teach AI systems new things, but this has limits. The authors came up with a clever way to make AI learn from easier problems and apply that learning to harder ones. This means AI can improve beyond what humans are capable of. They tested their idea and found it works really well, allowing AI systems to solve math problems that are too hard for humans. |
Keywords
* Artificial intelligence * Alignment * Generalization * Reinforcement learning * Supervised
