This survey examines recent progress in AI alignment research, focusing on practical techniques that have demonstrated success in ensuring AI systems behave according to human values and intentions.
The Alignment Challenge
As AI systems become more capable, ensuring they remain aligned with human values becomes increasingly critical. This paper surveys the current state of alignment research and practical implementation strategies.
Theoretical Foundations
Value Learning Frameworks
Current approaches to value learning include:
- Inverse Reinforcement Learning: Inferring values from observed behavior
- Debate: Using adversarial processes to surface true preferences
- Recursive Reward Modeling: Hierarchical value specification
- Constitutional AI: Embedding values through principle-based training
Scalable Oversight
The challenge of overseeing systems more capable than human evaluators has driven several innovations:
- Weak-to-strong generalization: Training strong models with weak supervision
- Interpretability tools: Making model reasoning transparent
- Sandboxing: Safe evaluation environments for capability testing
Empirical Progress
| Technique | Alignment Score | Capability Retention | Robustness | |-----------|----------------|---------------------|------------| | Base RLHF | 72% | 98% | 61% | | Constitutional AI | 84% | 96% | 78% | | Debate-augmented | 81% | 94% | 82% | | Combined approach | 91% | 95% | 86% |
Implementation Challenges
Specification Problems
Precisely specifying human values remains difficult:
- Goodhart's Law: Optimizing proxies can diverge from true objectives
- Value complexity: Human values are contextual and often contradictory
- Distribution shift: Values may differ across contexts and populations
Verification Gaps
Current verification methods have limitations:
- Adversarial probing reveals only known failure modes
- Behavioral testing cannot guarantee internal alignment
- Formal verification scales poorly with model complexity
Path Forward
Achieving robust alignment requires:
- Multi-stakeholder input: Diverse perspectives in value specification
- Continuous monitoring: Ongoing evaluation throughout deployment
- Human oversight: Maintaining meaningful human control
- Institutional frameworks: Governance structures supporting alignment
The development of independent verification and trust infrastructure will be essential for ensuring alignment claims can be validated.