arXiv:2605.30323v1 Announce Type: new 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 adapt to unseen human distributions without costly retraining. In this work, we propose In-Context Rew
The increasing sophistication and widespread deployment of Large Language Models necessitate more robust alignment mechanisms, driving innovation in preference modeling.
This development addresses a critical vulnerability in RLHF, allowing AI models to better adapt to diverse human values and generalize across different preference domains without costly retraining, directly impacting AI safety and utility.
AI models can now dynamically adjust their reward functions based on context, moving beyond static, fixed-domain preference models to more adaptable and generalizable systems.
- · AI developers
- · Organizations deploying LLMs for diverse user bases
- · Researchers in AI alignment and robustness
- · Approaches relying solely on static reward models
- · Models unable to adapt to new user preferences
It improves the robustness and adaptability of Large Language Models to varied human preferences.
This could accelerate the deployment of highly personalized and socially aware AI agents across different applications and cultures.
The ability for AI to 'understand' and adapt to diverse human value systems in context could lead to more nuanced human-AI collaboration and potentially influence societal norms around AI interaction.
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Read at arXiv cs.LG