The landscape of advanced AI research is evolving rapidly. While much of the discourse around AI safety, security, and governance has traditionally centred on alignment at the pre-training stage, recent advancements suggest a paradigm shift. Increasingly, cutting-edge AI models focus on fine-tuning rather than pre-training, necessitating a re-evaluation of where alignment efforts should be concentrated.
The Historical Focus on Pre-Training
AI alignment has largely been associated with pre-training, where models learn from vast amounts of raw data. Early safety discussions emphasized concerns such as bias, misinformation, and ethical constraints within the training dataset. This focus was valid when the majority of model intelligence was determined during pre-training. However, as models have become more capable, the landscape of AI development has transformed.
The Shift Toward Fine-Tuning
Recent advancements highlight the growing importance of fine-tuning, where models are refined for specific tasks, behaviours, and ethical considerations. Fine-tuning involves reinforcement learning, human feedback (RLHF), and domain-specific training, significantly shaping a model’s final deployment behaviour. Given this shift, AI alignment discussions need to move beyond pre-training and address how models are refined post-training to ensure safe, secure, and aligned outputs.
Why AI Safety, Security, and Governance Must Adapt
1. More Direct Impact
Training on high-quality, curated datasets to improve task-specific performance.
2. Customization Risks
Optimizing responses based on feedback mechanisms, often using reward modelling.
3. Regulatory Challenges
Governing fine-tuning requires new frameworks, as control over pre-trained models does not guarantee control over their fine-tuned iterations.
AI is advancing rapidly, and so are the risks and regulatory demands associated with its use. VE3’s whitepaper on AI safety, security & governance delves deep into the intricacies of responsible AI deployment, equipping organizations to build robust, compliant, and ethical AI solutions that are sustainable and secure.
A Call for New Standards in Fine-Tuning
To maintain responsible AI development, the community must:
- Develop robust governance frameworks addressing fine-tuning risks.
- Establish auditing mechanisms for post-training modifications.
- Promote transparency in how models are adapted and deployed.
- Shift discussions from theoretical alignment at pre-training to practical safeguards in fine-tuning.
Conclusion
The evolution of AI research demands a corresponding evolution in alignment discourse. As fine-tuning shapes AI behaviour, stakeholders must pivot their focus toward ensuring safe, secure, and governed post-training refinements. AI safety is no longer just about how models are trained—it’s about how they are fine-tuned for real-world applications.
VE3 is committed to helping organizations develop advanced AI solution. We provide tools and expertise that align innovation with impact. Together, we can create AI solutions that work reliably, ethically, and effectively in the real world. Contact us or visit us for a closer look at how VE3 can drive your organization’s success. Let’s shape the future together.