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Swap-guided Preference Learning for Personalized Reinforcement Learning from Human Feedback

arXiv:2603.12595v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is a widely used approach to align large-scale AI systems with human values. However, RLHF typically assumes a single, universal reward, which overlooks diverse preferences and limits personalization. Variational Preference Learning (VPL) seeks to address this by introducing user-specific latent variables. Despite its promise, we found that VPL suffers from posterior collapse. While this phenomenon is well known in VAEs, it has not previously been identified in preference learning frameworks. Under sparse preference data and with overly expressive decoders, VPL may cause latent variables to be ignored, reverting to a single-reward model. To overcome this limitation, we propose Swap-guided Preference Learning (SPL). The key idea is to construct fictitious swap annotators and use the mirroring property of their preferences to guide the encoder. SPL introduces three components: (1) swap-guid

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Gihoon Kim, Euntai Kim · · 1 min read · 8 views
#cs.LG #cs.AI

arXiv:2603.12595v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is a widely used approach to align large-scale AI systems with human values. However, RLHF typically assumes a single, universal reward, which overlooks diverse preferences and limits personalization. Variational Preference Learning (VPL) seeks to address this by introducing user-specific latent variables. Despite its promise, we found that VPL suffers from posterior collapse. While this phenomenon is well known in VAEs, it has not previously been identified in preference learning frameworks. Under sparse preference data and with overly expressive decoders, VPL may cause latent variables to be ignored, reverting to a single-reward model. To overcome this limitation, we propose Swap-guided Preference Learning (SPL). The key idea is to construct fictitious swap annotators and use the mirroring property of their preferences to guide the encoder. SPL introduces three components: (1) swap-guided base regularization, (2) Preferential Inverse Autoregressive Flow (P-IAF), and (3) adaptive latent conditioning. Experiments show that SPL mitigates collapse, enriches user-specific latents, and improves preference prediction. Our code and data are available at https://github.com/cobang0111/SPL

Executive Summary

This article addresses the limitation of traditional Reinforcement Learning from Human Feedback (RLHF) by introducing Swap-guided Preference Learning (SPL), a novel approach that mitigates posterior collapse in Variational Preference Learning (VPL). SPL constructs fictitious swap annotators and uses their mirroring property to guide the encoder, incorporating three key components: swap-guided base regularization, Preferential Inverse Autoregressive Flow (P-IAF), and adaptive latent conditioning. The proposed method is shown to improve preference prediction, enrich user-specific latents, and reduce collapse in experiments. While the work is promising, it is essential to consider the sparse preference data and overly expressive decoders that may cause VPL to revert to a single-reward model.

Key Points

  • ▸ Swap-guided Preference Learning (SPL) addresses the limitation of traditional RLHF by mitigating posterior collapse in Variational Preference Learning (VPL).
  • ▸ SPL constructs fictitious swap annotators and uses their mirroring property to guide the encoder.
  • ▸ The proposed method incorporates three key components: swap-guided base regularization, Preferential Inverse Autoregressive Flow (P-IAF), and adaptive latent conditioning.

Merits

Strength of Improving Preference Prediction

SPL is shown to improve preference prediction in experiments, demonstrating its effectiveness in addressing the limitation of traditional RLHF.

Enrichment of User-Specific Latents

The proposed method enriches user-specific latents, providing a more accurate representation of individual preferences.

Mitigation of Posterior Collapse

SPL mitigates posterior collapse, a phenomenon that occurs when VPL reverts to a single-reward model due to sparse preference data or overly expressive decoders.

Flexibility and Customization

The use of fictitious swap annotators allows for flexibility and customization in the training process, making SPL a more adaptable approach.

Demerits

Limitation of Sparse Preference Data

SPL may struggle with sparse preference data, which can cause VPL to revert to a single-reward model.

Oversimplification of Complex Preferences

The proposed method may oversimplify complex preferences, which can lead to inaccurate representations of individual preferences.

Expert Commentary

The proposed method is a significant contribution to the field of preference learning, addressing a critical limitation of traditional RLHF. However, it is essential to consider the potential limitations of the method, such as sparse preference data and oversimplification of complex preferences. Furthermore, the work highlights the importance of addressing posterior collapse in VAEs and provides a novel approach to mitigate this phenomenon. As the field continues to evolve, it will be essential to explore the scalability and applicability of SPL in real-world scenarios.

Recommendations

  • ✓ Further research is needed to explore the scalability and applicability of SPL in real-world scenarios.
  • ✓ The proposed method should be evaluated on a diverse range of datasets to assess its effectiveness in different contexts.

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