Principled Personalization and Preference Aggregation for Reinforcement Learning from Heterogeneous Human Feedback

Core Concepts

This paper proposes two frameworks to address the challenges of heterogeneous human preferences in Reinforcement Learning from Human Feedback (RLHF): a personalization-based framework and a human preference aggregation-based framework. The personalization-based framework leverages representation learning and clustering techniques to learn personalized reward models, while the aggregation-based framework employs social choice theory to aggregate diverse and potentially strategic human preferences into a single reward model.

Abstract

The paper addresses the challenge of handling heterogeneous human preferences in Reinforcement Learning from Human Feedback (RLHF), which is an important technique for aligning AI systems with human values.
The personalization-based framework consists of two approaches:

Representation-learning-based Personalization: This approach leverages comparison data from a diverse set of humans to enhance the accuracy of representation learning, leading to better sample complexity for estimating personalized reward models.

Clustering-based Personalization: This approach learns clustered reward functions and personalizes users' reward models within each cluster, providing sample complexity guarantees.

The aggregation-based framework also consists of two approaches:

Reward Aggregation with Comparison Data: This approach first estimates the parameters for each individual's reward model using their preference comparison data, and then aggregates the reward models using a family of reward aggregation rules, including those based on utilitarianism and Leximin.

Preference Aggregation with Probabilistic Opinion Data: This approach directly aggregates the diverse probabilistic preferences into a consensus preference, without assuming a relationship between human reward and preference. It also develops a mechanism to handle strategic human labelers who may benefit by reporting untruthful preferences.

The paper provides theoretical analyses, including sample complexity guarantees, for the proposed frameworks and approaches. It also establishes a near-optimal lower bound for the sub-optimality gap of personalization, demonstrating the tightness of the analysis.

Stats

The underlying true reward can be represented as r⋆
i (·) = ⟨ψ⋆(φ(·)),θ⋆
i ⟩ for some representation function ψ⋆ and ∥θ⋆
i ∥2 ≤B for each individual i ∈[N].
The concentrability coefficient, Cr(Gr,πtar,µref,i), reflects the concept of "single-policy concentrability" and is commonly assumed to be bounded in the offline RL literature.
The matrix Θ⋆= [θ⋆
1,··· ,θ⋆
N] ∈Rk×N satisfies σ2
k (Θ⋆) ≥Ω(N/k), indicating "diverse" human reward functions.

Quotes

"Contrary to the assumption of "homogeneity" in reward valuation, humans assign "heterogeneous" reward values to the same question-and-answer pairs, especially for sensitive and open-ended questions, depending on their background."
"Even when acknowledging the heterogeneity of human preferences and aggregating them carefully to learn a single reward model, there is a challenge that has not received enough attention in the RLHF literature: humans are by nature rational (of certain degrees) and strategic, with their own objectives to optimize."

Key Insights Distilled From

Principled RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation

by Chanwoo Park... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2405.00254.pdf

Deeper Inquiries

How can the proposed frameworks be extended to handle dynamic or evolving human preferences over time

To handle dynamic or evolving human preferences over time, the proposed frameworks can be extended by incorporating online learning techniques. By continuously updating the reward models and policy based on real-time feedback, the systems can adapt to changes in human preferences. This can be achieved by implementing algorithms that can learn incrementally from new data, such as online reinforcement learning algorithms like Online Q-learning or Online Policy Gradient methods. Additionally, the frameworks can include mechanisms to detect and adapt to shifts in preferences, such as drift detection algorithms that can trigger model updates when significant changes are detected. By integrating these dynamic learning capabilities, the systems can effectively adjust to evolving human preferences over time.

How can the personalization-based and aggregation-based frameworks be combined to leverage the strengths of both approaches

The personalization-based and aggregation-based frameworks can be combined to create a more robust and effective approach for reinforcement learning from heterogeneous human feedback. By leveraging the strengths of both approaches, the system can benefit from the personalized insights while also aggregating diverse preferences in a principled manner. One way to combine these frameworks is to use personalized models to capture individual nuances in preferences and then aggregate these personalized models using social choice theory principles. This hybrid approach can provide a comprehensive understanding of human preferences while ensuring fairness and accuracy in the fine-tuning process. By integrating personalization and aggregation techniques, the system can achieve a balance between individualized learning and collective decision-making.

What are the potential ethical implications of using strategic human feedback in the fine-tuning of large language models, and how can these be addressed

The use of strategic human feedback in the fine-tuning of large language models can raise ethical concerns related to bias, manipulation, and fairness. Strategic human labelers may provide untruthful feedback to influence the model's output in their favor, leading to biased or inaccurate results. To address these ethical implications, several measures can be implemented. Firstly, transparency and accountability in the feedback process can help mitigate strategic behavior by making the feedback process more transparent and traceable. Providing clear guidelines and incentives for truthful feedback can also discourage manipulative practices. Additionally, incorporating mechanisms for detecting and filtering out untruthful feedback, such as outlier detection algorithms or consistency checks, can help maintain the integrity of the feedback data. Overall, ensuring transparency, fairness, and integrity in the feedback collection process is essential to mitigate the ethical implications of strategic human feedback in model fine-tuning.