Capturing Individual Human Preferences with Reward Features (2503.17338v1)

Abstract: Reinforcement learning from human feedback usually models preferences using a reward model that does not distinguish between people. We argue that this is unlikely to be a good design choice in contexts with high potential for disagreement, like in the training of LLMs. We propose a method to specialise a reward model to a person or group of people. Our approach builds on the observation that individual preferences can be captured as a linear combination of a set of general reward features. We show how to learn such features and subsequently use them to quickly adapt the reward model to a specific individual, even if their preferences are not reflected in the training data. We present experiments with LLMs comparing the proposed architecture with a non-adaptive reward model and also adaptive counterparts, including models that do in-context personalisation. Depending on how much disagreement there is in the training data, our model either significantly outperforms the baselines or matches their performance with a simpler architecture and more stable training.

Capturing Individual Human Preferences with Reward Features: An Expert Perspective

In the exploration of adapting reinforcement learning models to better capture individual human preferences, the paper "Capturing Individual Human Preferences with Reward Features" presents a compelling approach that has significant implications for the field of machine learning, particularly in the training of LLMs. Conducted by researchers from Google DeepMind, the paper probes into the fundamental design flaw of treating human preferences as homogenous in reinforcement learning from human feedback (RLHF).

Overview and Methodology

The crux of the paper is the development of a method that allows a reward model to be specialized to individuals or groups of people, challenging the typical one-size-fits-all approach. The proposed method acknowledges the variability in human preferences and seeks to capture these through a set of general reward features that can be linearly combined to reflect individual preferences. The authors present an architecture called the Reward-Feature Model (RFM), which not only adapts swiftly to new users with minimal data but remains robust even in training environments characterized by high disagreement.

The methodology involves two critical phases: training and adaptation. During training, the model leverages a shared set of parameters, alongside individual-specific coefficients, to discern the common reward features across the dataset. The adaptation phase simplifies to a logistic regression problem, wherein only the coefficients associated with the reward features are tuned to a new user's data, thus streamlining the personalization process.

Key Findings

The authors provide a series of experiments that validate the effectiveness of RFMs. Notably, the results show that RFMs significantly outperform non-adaptive baselines in settings where human preferences are diverse and often conflicting. In experiments simulating both homogeneous and heterogeneous rater environments using the UltraFeedback dataset, RFMs demonstrated consistent adaptability, aligning well with user-specific preferences even when these were not represented in the training cohort. Additionally, the RFM approach outperformed in-context personalization methods from prominent LLMs when the number of adaptation examples was limited, underscoring its efficiency and potential for rapid personalization in practical applications.

Implications and Future Directions

The implications of this research span both the theoretical and practical domains. By effectively decoupling the features that influence human preference from the specific adaptations required for new users, this approach provides a scalable solution to personalized AI. Theoretically, it paves the way for more nuanced interpretations of user-centric models, encouraging further exploration into preference modeling that respects individual differences rather than averaging them out.

Practical implications include the enhancement of LLMs by offering personalized experiences that accommodate individual user tastes and contexts, addressing potential dissatisfaction that might arise from generic responses. This is particularly relevant in applications involving conversational agents or content recommendation systems, where subjective user criteria significantly dictate the success of the interaction.

Future research could explore the integration of RFMs with more complex LLM architectures and extend the concepts to other modalities such as images and sound, potentially updating the training protocols to embrace a multi-modal approach. Another avenue is to explore the active learning paradigms to enhance the efficiency and accuracy of the adaptation phase, thus minimizing the required number of user interactions for effective personalization.

In conclusion, the proposed approach of using reward features to model individual preferences introduces a needed dimension to RLHF, aligning machine outputs more closely with human expectations and improving the human-AI interaction experience. Moving forward, the adoption of such models is likely to lead to more sophisticated and user-responsive AI systems, marking a significant step in the journey towards truly intelligent personalized machines.