Generalizable Reward Model (GRM): An Efficient AI Approach to Improve the Generalizability and Robustness of Reward Learning for LLMs
What are the two primary methods for aligning large models?
The two primary methods for aligning large models are supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). SFT involves training a model on a labeled dataset to learn input-output mappings, while RLHF uses human feedback to guide the model's learning process and optimize its behavior2.
How does RLHF generalize the reward model?
RLHF generalizes the reward model by training it on human preference data, allowing it to guide the learning process towards aligning with human values and intentions2. This enables the model to produce more helpful and harmless responses. The reward model is trained as a proxy for human preferences to drive reinforcement learning optimization, and can generalize to new prompt-response pairs2. However, challenges include incorrect and ambiguous preference pairs in the dataset, and reward models trained on specific distributions may struggle to generalize to examples outside that distribution.
What is "overoptimization" in reward modeling?
Overoptimization in reward modeling refers to the issue of a reward model overfitting to the training data and having difficulty generalizing to unseen data. This can lead to the model performing well on the training set but failing to accurately predict rewards for new data, hindering the overall performance of the model.