Glossary
Alignment Research
The field studying how to build AI systems whose goals, values, and behaviors remain beneficial and consistent with human intentions as the systems become more capable.
Alignment research addresses one of the central challenges in AI development: ensuring that AI systems pursue goals and exhibit behaviors that are beneficial to humanity, and that this alignment persists and scales as systems become more capable. The concern is not just that current systems behave well, but that the properties that make them beneficial do not break down as capability increases.
The alignment problem arises because the objectives used to train AI systems are necessarily incomplete specifications of what we actually want. A system optimized for a proxy objective — one that correlates with what we want in the training distribution — may pursue that proxy in ways that diverge from our actual intentions when applied to novel situations or when the system is capable enough to exploit the gap between proxy and true objective.
Core research directions
Reward modeling and specification: How do we specify objectives that capture what we actually want, rather than proxies that correlate with it in training? Inverse reward design, cooperative inverse reward design, and debate approaches each offer different answers.
Scalable oversight: How do humans supervise AI systems that can perform tasks beyond human competence? Amplification, debate, and recursive reward modeling are proposed approaches. The challenge intensifies as systems become more capable.
Interpretability: How do we understand what AI systems are actually computing — what representations they have developed, what objectives they are pursuing, what reasoning processes produce their outputs? Understanding internal structure is necessary for detecting misalignment before it becomes consequential.
Constitutional methods: Using explicit value specifications — "constitutions" of principles — during training to shape model behavior. The AI is trained to critique and revise its outputs against these principles, internalizing them rather than treating them as external constraints.
Formal verification: Proving mathematical properties about model behavior rather than relying on empirical evaluation. Applicable to a subset of alignment-relevant properties that can be expressed precisely.
How Webbeon approaches Alignment Research
Webbeon's alignment work is integrated throughout the Odyssey development process rather than treated as a separate safety layer:
- Reward modeling that incorporates uncertainty about human preferences
- Constitutional training that encodes behavioral principles into the model architecture
- Formal verification of a growing set of behavioral properties as verification methods scale
- Capability-tiered deployment that requires stronger alignment evidence before releasing more capable model versions
- Published research contributing to the broader alignment research community
Key facts
- Webbeon treats alignment as a research problem, not a compliance checkbox — the field's open questions remain open
- The tension between capability and alignment is real: capabilities that make systems more useful also create new ways systems could be misaligned
- Alignment research is a long-horizon project; current techniques are adequate for current capability levels but may not scale
- Webbeon's policy is to publish alignment research to advance the field, not to hoard techniques as competitive advantages