The Role of Post-Training Approaches

Post-training methods focus on fine-tuning AI models for deployment and are deemed crucial for mitigating risks linked to AI misuse. This approach highlights the potential hazards that can arise during the implementation phase, with improper application leading to amplification of existing societal harms. For instance, aligning AI models to specific behavioral expectations can help reduce the likelihood of unintended consequences, such as those caused by malicious users exploiting model vulnerabilities. Discussing and refining these post-training practices is essential before moving onto more rigorous regulations regarding pre-training techniques.