Inference-Time Policy Alignment for Fair Reinforcement Learning
Proposes inference-time policy alignment for reinforcement learning agents to achieve fairness without retraining the base policy Introduces a multiplicative policy shaping framework that adjusts action probabilities using action-dependent welfare scores Formalizes inference-time fairness alignment as a policy shaping problem, inspired by inference-time alignment techniques in large language models Demonstrates that the approach substantially improves welfare-based fairness objectives while pres
Analysis
TL;DR
- Proposes inference-time policy alignment for reinforcement learning agents to achieve fairness without retraining the base policy
- Introduces a multiplicative policy shaping framework that adjusts action probabilities using action-dependent welfare scores
- Formalizes inference-time fairness alignment as a policy shaping problem, inspired by inference-time alignment techniques in large language models
- Demonstrates that the approach substantially improves welfare-based fairness objectives while preserving core task performance across multiple domains
- The framework is general and compatible with any deep RL agent, requiring no modification to base policy parameters
Why It Matters
This work addresses a critical practical challenge in deploying RL agents: the rigidity of trained policies when new fairness or stakeholder preferences emerge post-deployment. By enabling inference-time alignment without costly retraining, it offers a flexible, efficient pathway for making RL systems more adaptable and ethically aligned in real-world settings where preferences may evolve or were unknown during training.
Technical Details
- Multiplicative Policy Shaping Framework: Adjusts action probabilities multiplicatively using action-dependent welfare scores, steering the policy toward fairness objectives without touching base policy parameters
- Formalization as Policy Shaping: Inference-time fairness alignment is cast as a policy shaping problem, bridging concepts from LLM inference-time alignment to the RL domain
- Model-Agnostic Design: The framework is general-purpose and compatible with any deep RL agent architecture, not tied to a specific algorithm
- Welfare-Based Fairness Objectives: Focuses on welfare-based metrics to quantify and optimize fairness during deployment
- Multi-Domain Evaluation: Extensive experiments across multiple domains validate both fairness improvements and preservation of core task performance
Industry Insight
- Organizations deploying RL agents should consider inference-time alignment as a cost-effective alternative to full retraining when fairness or preference requirements change post-deployment
- The model-agnostic nature of this approach means existing RL infrastructure can be enhanced with fairness alignment with minimal engineering overhead
- As regulatory and ethical scrutiny of AI systems intensifies, inference-time alignment techniques will become essential tools for maintaining compliance without sacrificing performance
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