A First-Order Logic-Based Alternative to Reward Models in RLHF
Published:Dec 16, 2025 05:15
•1 min read
•ArXiv
Analysis
This article proposes a novel approach to Reinforcement Learning from Human Feedback (RLHF) by replacing reward models with a system based on first-order logic. This could potentially address some limitations of reward models, such as their susceptibility to biases and difficulty in capturing complex human preferences. The use of logic might allow for more explainable and robust decision-making in RLHF.
Key Takeaways
- •Proposes a first-order logic-based alternative to reward models in RLHF.
- •Aims to address limitations of reward models, such as bias and complexity.
- •Suggests potential for more explainable and robust decision-making in RLHF.
Reference
“The article is likely to delve into the specifics of how first-order logic is used to represent human preferences and how it is integrated into the RLHF process.”