Summary of Copr: Continual Learning Human Preference Through Optimal Policy Regularization, by Han Zhang et al.
COPR: Continual Learning Human Preference through Optimal Policy Regularization
by Han Zhang, Lin Gui, Yuanzhao Zhai, Hui Wang, Yu Lei, Ruifeng Xu
First submitted to arxiv on: 24 Oct 2023
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 paper, researchers address the limitations of Reinforcement Learning from Human Feedback (RLHF) in improving pre-trained Language Models (LMs). Current RLHF-based LMs require full retraining for novel queries or feedback, which is impractical due to time and computational resources required. To overcome this, they propose Continual Optimal Policy Regularization (COPR), a single-learning-phase method that mitigates Catastrophic Forgetting (CF) without needing complex reinforcement learning. COPR shares the ability with RLHF to learn from unlabeled data, making it suitable for continual learning without human feedback. Experimental results show COPR outperforms strong Continuous Learning (CL) baselines in consistently aligning with human preferences on incremental tasks and domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers found a way to make language models better at following what humans want. They used a technique called Reinforcement Learning from Human Feedback, but it has some big problems. For example, it takes a lot of time and computer power to retrain the model every time something new comes along. To fix this, they created a new method called Continual Optimal Policy Regularization. This way, the language model can keep getting better without needing as much help from humans. |
Keywords
* Artificial intelligence * Continual learning * Language model * Regularization * Reinforcement learning * Reinforcement learning from human feedback * Rlhf
