The Anthropic Fellows Program is a research initiative at Anthropic focused on developing methods for automating AI safety and alignment research. The program addresses a critical challenge in AI development: the need to scale high-quality alignment research as AI systems become increasingly capable. By automating components of the alignment research process itself, the program demonstrates that outcome-gradable AI research problems can be systematized and delegated to automated systems 1).2)
The Fellows Program operates at the intersection of two pressing concerns in AI development: the exponential growth in AI capabilities and the corresponding need for robust safety assurance. Rather than relying solely on human researchers to identify and solve alignment challenges, the program explores how machine learning itself can be leveraged to accelerate alignment research 3).
The core objective involves automating the process of conducting AI safety research on well-defined, outcome-gradable problems. This approach recognizes that many alignment research tasks involve iterative hypothesis testing, evaluation, and refinement—processes amenable to automation. By demonstrating practical examples of automated alignment research, the program establishes proof-of-concept evidence that scaling alignment research capability is feasible.
The program's methodology centers on identifying alignment research problems that have clear evaluation criteria and measurable outcomes. Outcome-gradable problems—those where success or failure can be objectively determined—form the foundation of the automation approach. This constraint ensures that automated systems can receive meaningful feedback signals necessary for effective learning and optimization.
The automated alignment research process typically involves: problem specification, hypothesis generation, experimental design, result evaluation, and iterative refinement. By automating these components, the program accelerates the research cycle while maintaining scientific rigor. This approach parallels broader trends in machine-assisted scientific discovery, where AI systems augment rather than replace human expertise 4).
The Anthropic Fellows Program contributes to multiple dimensions of AI alignment research. Automated approaches to safety research enable faster iteration on proposed solutions, more comprehensive exploration of technical problem spaces, and systematic evaluation of alignment techniques. The program has demonstrated that automation can effectively handle research tasks including safety evaluation, technique validation, and hypothesis testing 5).
Practical applications emerging from the program's work include automated evaluation frameworks for alignment techniques, systematic exploration of safety-critical design spaces, and scalable methods for validating AI safety properties. These applications extend beyond theoretical validation to support practical deployment considerations for large language models and other advanced AI systems.
The program's work on automating alignment research carries significant implications for the broader AI safety field. If alignment research itself can be partially automated, this addresses a fundamental scaling challenge: as AI systems become more capable, the difficulty of ensuring their safety through human-directed research increases proportionally. Automating alignment research provides a potential solution pathway, though challenges remain regarding the completeness of automated approaches and the irreducible role of human oversight in safety-critical domains.
The Fellows Program operates within Anthropic's broader commitment to AI safety, which emphasizes constitutional AI principles, responsible AI scaling, and systematic evaluation of alignment properties across different model sizes and capabilities 6).
As of 2026, the Anthropic Fellows Program continues to produce research demonstrating practical automation of outcome-gradable AI research problems. The program's contributions advance both the technical understanding of alignment automation and the empirical evidence that scaling safety research is achievable through systematic automation approaches. Ongoing work explores expanding the scope of automatable alignment research problems and improving the reliability of automated safety evaluation systems.