Alignment Challenges › Alignment Challenges

As language models grow more capable, a central question moves from theoretical to urgent: how do we ensure these systems reliably do what we intend? This is the alignment problem, and it combines technical research with philosophical questions and engineering practice. A model that generates fluent text is impressive. A model that pursues its user's intended goals, handles edge cases gracefully, and behaves safely in novel situations is far harder to build.

Alignment is not a single challenge but a cluster of interrelated problems. Some are familiar from software engineering: specifications are hard to write, tests are incomplete, and systems find unexpected shortcuts. Others are more novel: reward models trained on human feedback inherit human biases and blind spots; objectives that seem reasonable in training can generalize in unexpected ways at deployment; and the very capabilities that make large models useful also make their failure modes harder to anticipate. The chapters in earlier parts of this handbook have surveyed the frontier of what these systems can do. This chapter examines the challenges of making sure that what they do matches what we want.

Consider the arc of progress in language AI over the past decade. Models grew from narrow classifiers to systems that can write code, reason about mathematics, engage in extended dialogue, and pass professional examinations. Each of these capability jumps produced concrete benefits and also new failure modes. A model that can reason about chemistry can also reason about dangerous chemistry. A model that can write persuasive prose can also write persuasive misinformation. The value of these systems is inseparable from their potential for misuse, and the same mechanisms that produce capability also produce risk.

We will cover four interconnected topics. First, scalable oversight addresses the practical difficulty of supervising systems that are becoming smarter than their supervisors. Second, the alignment tax examines the tradeoff between capability and safety: does making a model safer necessarily make it less useful? Third, goal mis-specification explores the many ways in which the objectives we give to models can diverge from the objectives we intended. Fourth, we look at the long-term alignment research agenda: what open problems remain, and what approaches show the most promise.