arXiv:2505.10892v2 Announce Type: replace Abstract: Post-training LLMs with RLHF and preference optimization methods (e.g., DPO, IPO) has greatly improved alignment, yet these approaches assume a single objective. In reality, humans express multiple, often conflicting objectives, such as helpfulness and harmlessness, with no natural scalarization. We study the multi-objective preference alignment problem, where a policy must balance several objectives simultaneously. We propose Multi-Objective Preference Optimization (MOPO), a constrained KL-regularized framework that maximizes a primary objec
Current generative AI models trained with single-objective optimization often struggle with balancing conflicting human values, necessitating a more sophisticated approach as AI integration deepens.
This development addresses a fundamental limitation in AI alignment, crucial for deploying more trustworthy and ethically sound generative models across sensitive applications.
AI models can now be optimized to juggle multiple, potentially conflicting human objectives simultaneously, moving beyond a simplistic scalarization of values.
- · AI developers
- · Ethical AI frameworks
- · Users of generative AI
- · Single-objective optimization methods
- · Companies with poorly aligned AI products
Generative AI models will become more nuanced and capable of exhibiting complex human-like judgments.
Public trust and acceptance of AI systems will likely increase as they demonstrate better alignment with human values.
The development of 'value-aligned' AI could accelerate, potentially leading to more advanced agentic systems with built-in ethical constraints.
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Read at arXiv cs.LG