Generalizable Reward Model (GRM): An Efficient AI Approach to Improve the Generalizability and Robustness of Reward Learning for LLMs
What are the primary methods for aligning AI models?
The primary methods for aligning AI models are supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). SFT involves training the model using labeled data to learn patterns and make accurate predictions. RLHF uses human feedback to guide the model's learning process and optimize its behavior, ensuring alignment with human values and goals. Both methods aim to make AI systems behave in line with human intentions and values, reducing the risks of misalignment1.
What challenges does RLHF face with unseen data?
RLHF faces challenges with unseen data primarily due to overoptimization or reward hacking, where the model overfits to the training data and fails to generalize well. This can result in poor performance when encountering out-of-distribution data. Increasing the size of the reward model and the amount of training data can help, but it is not practical in real-world situations.
What approaches mitigate overoptimization in RLHF?
Overoptimization in RLHF can be mitigated through several approaches, including:
- Increasing the size of the reward model and the amount of training data, although this may not be practical in real-world situations.
- Penalizing overly confident model outputs using label smoothing or SFT regularization.
- Using the Generalizable Reward Model (GRM), which employs text-generation regularization on hidden states to improve reward model performance and reduce overoptimization.
These methods aim to enhance the generalizability and robustness of reward learning for large language models.