Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging (2310.11564v1)

Abstract: While Reinforcement Learning from Human Feedback (RLHF) aligns LLMs with general, aggregate human preferences, it is suboptimal for learning diverse, individual perspectives. In this work, we study Reinforcement Learning from Personalized Human Feedback (RLPHF) problem, wherein LLMs are aligned to multiple (sometimes conflicting) preferences by modeling alignment as a Multi-Objective Reinforcement Learning (MORL) problem. Compared to strong single-objective baselines, we show that we can achieve personalized alignment by decomposing preferences into multiple dimensions. These dimensions are defined based on personalizations that are declared as desirable by the user. In this work, we show that they can be efficiently trained independently in a distributed manner and combined effectively post-hoc through parameter merging. The code is available at https://github.com/joeljang/RLPHF.

Analyzing Personalized Alignment of LLMs via Parameter Merging

The paper "Personalized Soups: Personalized LLM Alignment via Post-hoc Parameter Merging" proposes a novel approach to align LLMs with diverse individual preferences. Unlike standard Reinforcement Learning from Human Feedback (RLHF), which generally optimizes for aggregate human preferences, this paper addresses the Reinforcement Learning from Personalized Human Feedback (RL$\mathcal{P}$HF) by modeling it as a Multi-Objective Reinforcement Learning (MORL) problem. The significance of this work lies in its innovative methodology, termed "Personalized Soups," and its implications for the future interplay between AI systems and human nuances.

Core Contributions

1. MORL for Personalized Alignment: The authors propose handling the alignment to individual preferences as a MORL problem, which allows the model to dynamically adjust the weightage of multiple, sometimes conflicting, human preferences. This multi-objective approach contrasts with single-objective models and reveals a path to a more nuanced, user-driven interaction with LLMs.
2. Personalized Soups: A key novelty of this paper is the introduction of Personalized Soups, a method that enables post-hoc parameter merging. Here, the model parameters are not optimized simultaneously for all preferences. Instead, each preference is trained independently using Proximal Policy Optimization (PPO), and parameters are merged at inference time. This modular approach reduces the computational complexity from exponential to linear concerning the diverse set of preferences.
3. Empirical Validation: The paper presents empirical results that demonstrate the efficacy of transforming the alignment of LLMs to human preferences into a MORL problem, achieving more personalized and adaptable model outputs compared to traditional approaches like fine-tuning, RLHF, and simple prompting.

Theoretical and Practical Implications

• Scalability and Flexibility: One of the standout implications of this research is its scalability. Traditional methods require substantial retraining when new preferences or combinations thereof are introduced. Conversely, Personalized Soups offer a dynamic and flexible framework where integrating a new preference involves training a new model distinct to that preference and merging it parameter-wise, avoiding the need for full retraining.
• Future Directions in Personalization: The outlined approach could drive substantial progress in how LLMs are employed in personalized settings. For instance, personalization in customer support AI, educational tools, and interactive learning platforms can be fine-grained to cater to individual learning styles and preferences dynamically.
• Challenge of Fairness and Bias: While promising, the model raises concerns and opens avenues for research into fairness and bias. As models become more personalized, ensuring that personalization does not amplify biases inherent in the training data or oversimplifies complex user interactions becomes crucial.

Summation

This paper sets a foundational step towards tailoring AI systems to humans' multi-faceted needs by innovating on current reinforcement learning paradigms to accommodate personalized feedback fully. Future work could explore broader applications, additional modalities of personalization, and deeper integration with human feedback loops. The scalability of the proposed approach indicates a substantial transformation potential in how AI can interface with human preferences, but it also necessitates vigilance in ethical AI practices to ensure equitable outcomes for all user groups. As AI continues to evolve, methodologies like those proposed here are pivotal in crafting systems that genuinely reflect the diverse fabric of human experience.