Secrets of RLHF in Large Language Models Part II: Reward Modeling (2401.06080v2)

Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning LLMs with human values and intentions, enabling models to produce more helpful and harmless responses. Reward models are trained as proxies for human preferences to drive reinforcement learning optimization. While reward models are often considered central to achieving high performance, they face the following challenges in practical applications: (1) Incorrect and ambiguous preference pairs in the dataset may hinder the reward model from accurately capturing human intent. (2) Reward models trained on data from a specific distribution often struggle to generalize to examples outside that distribution and are not suitable for iterative RLHF training. In this report, we attempt to address these two issues. (1) From a data perspective, we propose a method to measure the strength of preferences within the data, based on a voting mechanism of multiple reward models. Experimental results confirm that data with varying preference strengths have different impacts on reward model performance. We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data. (2) From an algorithmic standpoint, we introduce contrastive learning to enhance the ability of reward models to distinguish between chosen and rejected responses, thereby improving model generalization. Furthermore, we employ meta-learning to enable the reward model to maintain the ability to differentiate subtle differences in out-of-distribution samples, and this approach can be utilized for iterative RLHF optimization.

The paper focuses on exploring Reinforcement Learning from Human Feedback (RLHF) and, in particular, the role of reward modeling in improving LLMs. RLHF is crucial for aligning LLMs with human values and ensuring that their outputs are helpful and benign, which has become increasingly important in the deployment of AI systems.

Background and Relevance

RLHF serves as a bridge between machine learning models and human intentions. The process generally involves collecting human preferences, training a reward model based on these preferences, and then optimizing the LLM using reinforcement learning to maximize the rewards. Despite its potential, RLHF faces challenges such as noise in human feedback data and the generalization capabilities of the reward model across different data distributions.

Key Challenges in Reward Modeling

1. Data Noise and Ambiguity: One of the main issues with reward models is dealing with incorrect or ambiguous preference data. This noise arises due to variability in human annotations. Researchers estimate the agreement among annotators to be between 60% and 70%, which implies significant inconsistency.
2. Generalization Limitations: Reward models often struggle to maintain their performance when applied to out-of-distribution (OOD) data, which refers to scenarios not covered by the initial training data. This shortcoming can destabilize the learning process and require new, costly preference data.

Proposed Solutions

The paper proposes solutions from both a data perspective and an algorithmic perspective to overcome these challenges:

1. Data Perspective:
   • Preference Strength Measurement: A novel voting mechanism using multiple reward models is introduced to assess the strength of preferences within data. This approach helps in identifying and mitigating the effects of incorrect and ambiguous preferences, allowing the use of high-quality preference data.
   • Label Flipping and Smoothing: Incorrect labels are flipped, and label smoothing techniques are used to make the model more robust to noise in preference data.
2. Algorithmic Perspective:
   • Contrastive Learning: This method is integrated to improve the model’s ability to distinguish between chosen and rejected responses, hence enhancing generalization.
   • Meta-Learning: By employing meta-learning, the reward model learns to transfer and adapt its knowledge to OOD examples, maintaining its ability to distinguish subtle differences in data.

Experimental Validation

The researchers validate their approaches by training multiple reward models and showing how they can effectively evaluate preference data, categorize it by strength, and enhance the stability and performance of models in both alignment tasks and iterative RLHF.

Pitfalls and Recommendations

The paper highlights several pitfalls in reward modeling:

• Overfitting to noise in preference data can degrade performance.
• Relying solely on generalization can destabilize the learning process.

To avoid these pitfalls, the paper recommends:

• Using adaptive margins in loss functions to weight preference data by its reliability.
• Employing label flipping and smoothing techniques to cleanse noisy data.

In summary, tackling RLHF challenges in LLMs involves carefully handling preference data and utilizing advanced learning techniques to ensure models align with human intentions and perform robustly across diverse scenarios.