Summary of Symmetric Reinforcement Learning Loss For Robust Learning on Diverse Tasks and Model Scales, by Ju-seung Byun et al.
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model Scales
by Ju-Seung Byun, Andrew Perrault
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Reinforcement learning (RL) is a challenging problem due to unstable training factors like moving targets and high gradient variance. Recent approaches, such as Reinforcement Learning from Human Feedback (RLHF) and Reinforcement Learning from AI Feedback (RLAIF), introduce additional difficulties. To address this, RL has borrowed techniques from supervised learning, including ensembles and layer normalization. This work improves RL training stability by adapting the reverse cross-entropy (RCE) loss for noisy data to define a symmetric RL loss. The proposed approach is demonstrated across various tasks and scales using Symmetric A2C (SA2C) and Symmetric PPO (SPPO), with notable performance improvements in SPPO with different hyperparameters. Furthermore, this method benefits large language models by improving performance in RLHF tasks like sentiment analysis and summarization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make a type of artificial intelligence called reinforcement learning more stable. Reinforcement learning is used to train AI systems that can learn from feedback, but it’s tricky because the training process can be unstable. The authors came up with a new way to improve this stability by using an idea from another area of computer science. They tested their approach on various tasks and found that it worked well. This could lead to better language models and other AI systems in the future. |
Keywords
» Artificial intelligence » Cross entropy » Reinforcement learning » Reinforcement learning from human feedback » Rlhf » Summarization » Supervised
