arXiv:2602.08819v2 Announce Type: replace Abstract: Reward models are central to aligning language models with human preferences via reinforcement learning (RL). As RL is increasingly applied to settings such as verifiable rewards and multi-objective alignment, RMs are expected to encode more complex and multifaceted preference distributions. However, classifier RMs remain static once trained, limiting their adaptability at test time. We propose Variational In-Context Reward Modeling (ICRM), a novel Bayesian reward modeling objective that enables test-time steerability via in-context preferenc
The increasing complexity of aligning large language models with human preferences, especially for multi-objective tasks and verifiable rewards, demands more sophisticated and adaptable reward mechanisms.
This development allows for test-time steerability of reward models, which is crucial for building more adaptable and precise AI agents that can respond to nuanced human instructions or changing objectives.
Reward models, typically static after training, can now be dynamically adjusted 'in-context' at test time, offering greater flexibility and control over AI behavior without retraining.
- · AI developers
- · AI-driven product companies
- · Reinforcement learning researchers
- · Developers reliant on static reward models
- · AI systems with rigid, non-adaptable alignment
AI agents become more capable of adapting their behavior to real-time feedback and shifting user priorities.
This improved adaptability could accelerate the deployment of autonomous AI agents in complex, dynamic environments.
The enhanced steerability of AI agents may lead to new ethical and control challenges as their in-the-moment decision-making becomes harder to predict statically.
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