arXiv:2606.13209v1 Announce Type: cross Abstract: Reward models are a key component of reinforcement learning from human feedback (RLHF), aligning language models toward both helpful and harmless behaviour. However, the internal mechanisms underlying these objectives and their conflicts remain poorly understood. We study alignment tension in reward models trained under helpfulness-only, harmlessness-only, and mixed-objective settings. We find that mixed-objective models often underperform single-objective models, indicating interference between objectives. Using activation-based methods, we id
The increasing sophistication and widespread deployment of large language models heighten the urgency to understand and mitigate tensions between 'helpful' and 'harmless' objectives.
Improving the alignment of AI models is critical for their safe and effective integration into society, directly impacting their commercial viability and ethical governance.
Our understanding of the internal mechanics and inherent conflicts within AI reward models is deepening, leading to more nuanced development strategies for AI alignment.
- · AI developers focused on safety
- · Ethical AI research institutions
- · Companies relying on responsible AI deployment
- · AI developers prioritizing speed over safety
- · Models exhibiting unexpected harmful behaviors
- · Users encountering misaligned AI systems
Research into AI alignment techniques, particularly for helpfulness and harmlessness, will accelerate.
New architectural designs for reward models, or even entirely new alignment paradigms, may emerge to address discovered conflicts.
Public trust and regulatory frameworks for AI could be significantly influenced by the ability of models to robustly demonstrate both helpful and harmless behavior.
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