Summary of Scalable Ensembling For Mitigating Reward Overoptimisation, by Ahmed M. Ahmed et al.
Scalable Ensembling For Mitigating Reward Overoptimisation
by Ahmed M. Ahmed, Rafael Rafailov, Stepan Sharkov, Xuechen Li, Sanmi Koyejo
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 In this research paper, scientists develop a novel approach to improve language modeling by utilizing human feedback. The goal is to create powerful AI models that can follow instructions effectively. However, they encountered an issue called overoptimization, where the model becomes too good at learning from a proxy reward and forgets about the real goal. Previous methods tried to solve this problem by averaging multiple reward models together, but this was too computationally expensive for large language models. To overcome this challenge, the researchers propose using a shared encoder with separate linear heads, achieving similar performance while reducing training time and memory requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating more powerful AI models that can understand what we want them to do. The problem they faced was that these models got too good at doing things slightly wrong, instead of focusing on getting the right answer. They tried a few different methods to solve this problem, but most were too slow and expensive for really big language models. To fix this, they came up with a new idea: using one part of the model (the encoder) that is shared among many smaller parts (the linear heads). This way, they can get good results while using fewer computer resources. |
Keywords
» Artificial intelligence » Encoder
