In-Context Reward Adaptation for Robust Preference Modeling

📰 ArXiv cs.AI

Learn to adapt reward models in RLHF for robust preference modeling across diverse domains

advanced Published 29 May 2026

Action Steps

Implement In-Context Reward Adaptation using a meta-learning approach to adapt to new domains
Train a reward model on a diverse set of human preferences to improve robustness
Evaluate the adapted reward model on unseen preference domains to measure generalization
Use the adapted reward model to fine-tune a Large Language Model for improved alignment with human preferences
Compare the performance of the adapted reward model with a static reward model to measure the improvement in robustness

Who Needs to Know This

ML researchers and engineers working on RLHF and LLMs can benefit from this technique to improve model robustness and generalization

Key Insight

💡 Adapting reward models in RLHF can improve robustness and generalization to unseen preference domains

Key Takeaways

Learn to adapt reward models in RLHF for robust preference modeling across diverse domains

Full Article

Title: In-Context Reward Adaptation for Robust Preference Modeling

Abstract:
arXiv:2605.30323v1 Announce Type: cross Abstract: Reinforcement Learning from Human Feedback (RLHF) typically relies on static reward models to align Large Language Models with human preferences. However, human values are inherently diverse and heterogeneous, and a single reward model often lacks the robustness required to generalize to unseen preference domains. While existing multi-reward frameworks attempt to address this, they are often restricted to a fixed set of known domains and fail to

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