In-Context Reward Adaptation for Robust Preference Modeling

Problem
This paper addresses the limitations of static reward models in Reinforcement Learning from Human Feedback (RLHF), which struggle to generalize across diverse and unseen human preference domains. Existing multi-reward frameworks are constrained to predefined domains and require extensive retraining to adapt to new distributions. This work is presented as a preprint and has not undergone peer review.

Method
The authors introduce In-Context Reward Adaptation, a transformer-based framework that dynamically infers reward structures from a limited number of preference demonstrations. The architecture builds on standard transformer models but incorporates human response time as an auxiliary input, which enhances the model’s ability to adapt to previously unseen preferences. The training process leverages in-context learning, allowing the model to adjust its reward predictions based on contextual cues from the input data. The authors do not disclose specific training compute or dataset sizes, but they emphasize the model’s capability to handle heterogeneous reward distributions effectively.

Results
The proposed method outperforms baseline models on several benchmarks, demonstrating significant improvements in robustness and adaptability. Specifically, the authors report a 15% increase in accuracy for preference prediction tasks compared to traditional static reward models. Additionally, the incorporation of response time as an auxiliary signal leads to a 20% reduction in prediction bias when evaluated against a diverse set of human feedback scenarios. These results indicate that In-Context Reward Adaptation provides a more flexible and scalable approach to aligning AI systems with human preferences.

Limitations
The authors acknowledge that their approach may still struggle with extreme outlier preferences that deviate significantly from the training demonstrations. They also note that the reliance on a small set of preference demonstrations may introduce noise, potentially affecting the model’s performance in highly variable contexts. Furthermore, the paper does not address the computational overhead associated with real-time adaptation during inference, which could limit practical deployment in resource-constrained environments.

Why it matters
This work has significant implications for the field of human-AI alignment, particularly in applications requiring nuanced understanding of human preferences, such as personalized content recommendation and interactive AI systems. By enabling models to adapt to diverse and previously unseen preferences without extensive retraining, this approach paves the way for more robust and flexible AI systems. The findings suggest that integrating auxiliary signals, like response time, can enhance the adaptability of machine learning models, which may inspire future research into multi-modal input integration for preference modeling.

Authors: Zhenyu Sun, Zheng Xu, Ermin Wei
Source: arXiv:2605.30323
URL: https://arxiv.org/abs/2605.30323v1