Summary of Uncertainty-penalized Reinforcement Learning From Human Feedback with Diverse Reward Lora Ensembles, by Yuanzhao Zhai et al.
Uncertainty-Penalized Reinforcement Learning from Human Feedback with Diverse Reward LoRA Ensembles
by Yuanzhao Zhai, Han Zhang, Yu Lei, Yue Yu, Kele Xu, Dawei Feng, Bo Ding, Huaimin Wang
First submitted to arxiv on: 30 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 explores the limitations of reinforcement learning from human feedback (RLHF) in aligning large language models (LLMs). Existing methods rely on KL regularization to address overoptimization, but this approach has its weaknesses. The authors propose uncertainty-penalized RLHF (UP-RLHF), which incorporates uncertainty regularization during RL-finetuning. To enhance uncertainty quantification, they introduce a diverse low-rank adaptation (LoRA) ensemble by maximizing the nuclear norm of LoRA matrix concatenations. This ensemble is used to optimize policy models utilizing penalized rewards, determined by both rewards and uncertainties provided by the diverse reward LoRA ensembles. The results demonstrate the effectiveness of this approach in quantifying reward uncertainty and mitigating overoptimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RLHF helps large language models match human preferences, but it has a problem called overoptimization. Existing solutions use KL regularization to fix this issue, but they have limitations too. Researchers propose a new method that adds uncertainty into the training process. This makes the model more aware of its own mistakes and helps it avoid overfitting. To make this work, they create an ensemble of smaller models that all agree on what’s important. They then use these small models to guide the main model in making better choices. This new approach works well on two real datasets and shows promise for helping large language models match human preferences. |
Keywords
* Artificial intelligence * Lora * Low rank adaptation * Overfitting * Regularization * Reinforcement learning from human feedback * Rlhf
