Implicit Safety Alignment from Crowd Preferences

2026-05-23 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The paper introduces Safe Crowd Preference-based Reinforcement Learning (Safe-CPRL), a hierarchical framework designed to extract and transfer implicit safety objectives from diverse crowd preference datasets to downstream reinforcement learning tasks. Traditional reward combination methods, which directly optimize a preference-learned reward with task rewards, are shown to have limitations, including sensitivity to weighting and performance degradation from user-specific components in imbalanced datasets. Safe-CPRL instead learns low-level, safety-aligned skills using a VAE-based latent skill model (Safe-VPL) or a CPL-based variant (Safe-CPL) from crowd preferences. A high-level policy then composes these skills to solve new tasks, maximizing only the downstream task reward. Experiments across six safe RL environments and a preliminary LLM-style task demonstrate that Safe-CPRL significantly reduces safety costs (e.g., 1.0 vs. 0.01–0.02 normalized cost) while maintaining task performance comparable to oracle methods that use ground-truth safety signals.

Key takeaway

For Machine Learning Engineers developing safe RL agents in complex, multi-objective environments, you should consider adopting a hierarchical skill composition approach like Safe-CPRL. This method effectively extracts implicit safety objectives from crowd preferences, even with imbalanced data, and transfers them to new tasks without requiring explicit safety rewards. By composing pre-learned, safety-aligned skills, your agents can achieve robust safety performance while maintaining task efficacy, mitigating the challenges of hand-specifying complete reward functions.

Key insights

A hierarchical RL framework can extract and compose implicit safety-aligned skills from diverse crowd preferences to solve new tasks safely.

Principles

• Direct reward combination struggles with user-specific components and weighting.
• Composing preference-aligned skills inherently enforces shared safety criteria.
• More accurate latent variable modeling leads to more versatile skills.

Method

Safe-CPRL uses a VAE (or CPL variant) to learn latent-conditioned low-level skills from crowd preferences. A high-level policy then composes these skills, regularized by a prior, to maximize downstream task rewards using offline or online RL.

In practice

• Use VAE-based skill discovery to model diverse user-preferred behaviors.
• Train a high-level policy to compose these skills for new tasks.
• Regularize high-level policy to stay within learned latent skill structure.

Topics

• Reinforcement Learning from Human Feedback
• Safety Alignment
• Crowd Preferences
• Hierarchical Reinforcement Learning
• Skill Discovery
• Large Language Models

Code references

• qianlin04/Implicit-Safety-Alignment-from-Crowd-Preferences
• WEIRDLabUW/vpl
• sfujim/TD3
• sfujim/TD3_BC
• liuzuxin/OSRL

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.