Summary of Rebel: Reinforcement Learning Via Regressing Relative Rewards, by Zhaolin Gao et al.
REBEL: Reinforcement Learning via Regressing Relative Rewards
by Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kianté Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, Wen Sun
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 proposes a new reinforcement learning (RL) algorithm called REBEL, which reduces policy optimization to regressing the relative reward between two completions of a prompt with respect to the policy. This approach enables a lightweight implementation and can be extended to handle offline data and intransitive preferences. Theoretical guarantees are matched with Natural Policy Gradient, allowing for convergence and sample complexity similar to existing RL algorithms. Empirically, REBEL achieves strong performance in language modeling and image generation tasks, outperforming or matching PPO and DPO while being simpler to implement and more computationally efficient. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary REBEL is a new way to solve reinforcement learning problems. Instead of using complicated methods like PPO, REBEL makes things simpler by looking at how good two different versions of something are compared to each other. This helps it work well with lots of different tasks, including language and image generation. It’s also faster and easier to use than some other algorithms. |
Keywords
» Artificial intelligence » Image generation » Optimization » Prompt » Reinforcement learning
