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Anthropic Alignment Science

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High quality. Established institution or organization with editorial oversight and accountability.

Rating inherited from publication venue: Anthropic

This is the central hub for Anthropic's Alignment team research output; useful for tracking ongoing publications and understanding the team's research agenda across evaluation, oversight, and safeguard development.

Metadata

Importance: 62/100homepage

Summary

This is the homepage for Anthropic's Alignment research team, which develops protocols and techniques to train, evaluate, and monitor highly capable AI models safely. The team focuses on evaluation and oversight, stress-testing safeguards, and ensuring models remain helpful, honest, and harmless even as AI capabilities advance beyond current safety assumptions. It aggregates links to key publications including work on alignment faking, reward tampering, and hidden objectives.

Key Points

  • Focuses on evaluation and oversight methods to verify model behavior under novel circumstances, including human-AI collaboration for claims verification.
  • Systematically stress-tests safeguards by probing for failure modes that may emerge with human-level or superhuman AI capabilities.
  • Key publications cover alignment faking, reward tampering generalizing to self-modification, and auditing models for hidden objectives.
  • Represents Anthropic's core alignment research agenda, complementing Interpretability, Societal Impacts, and Economic Research teams.
  • Research emphasizes that future AI systems may break assumptions behind current safety techniques, motivating proactive safety work now.

Cited by 1 page

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[Back to Overview](https://www.anthropic.com/research)

# Alignment

Future AI systems will be even more powerful than today’s, likely in ways that break key assumptions behind current safety techniques. That’s why it’s important to develop sophisticated safeguards to ensure models remain helpful, honest, and harmless. The Alignment team works to understand the challenges ahead and create protocols to train, evaluate, and monitor highly-capable models safely.

Research teams: [Alignment](https://www.anthropic.com/research/team/alignment) [Economic Research](https://www.anthropic.com/research/team/economic-research) [Interpretability](https://www.anthropic.com/research/team/interpretability) [Societal Impacts](https://www.anthropic.com/research/team/societal-impacts)

![Video thumbnail](https://www.anthropic.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F4zrzovbb%2Fwebsite%2Fc351e05137a3da7d475af1c36f705cb4ff4b2179-1440x810.png&w=3840&q=75)

### Evaluation and oversight

Alignment researchers validate that models are harmless and honest even under very different circumstances than those under which they were trained. They also develop methods to allow humans to collaborate with language models to verify claims that humans might not be able to on their own.

### Stress-testing safeguards

Alignment researchers also systematically look for situations in which models might behave badly, and check whether our existing safeguards are sufficient to deal with risks that human-level capabilities may bring.

![Video thumbnail](https://www.anthropic.com/_next/image?url=https%3A%2F%2Fcdn.sanity.io%2Fimages%2F4zrzovbb%2Fwebsite%2F97f7955c3bd6586216581f62569fae241b2c2cef-1920x1080.png&w=3840&q=75)

[**Claude’s Character** \\
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AlignmentJun 8, 2024\\
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Claude 3 was the first model with "character training"—alignment aimed at nurturing traits like curiosity, open-mindedness, and thoughtfulness.](https://www.anthropic.com/research/claude-character)

[AlignmentMar 13, 2025\\
**Auditing language models for hidden objectives** \\
How would we know if an AI system is "right for the wrong reasons"—appearing well-behaved while pursuing hidden goals? This paper develops the science of alignment audits by deliberately training a model with a hidden objective and asking blinded research teams to uncover it, testing techniques from interpretability to behavioral analysis.](https://www.anthropic.com/research/auditing-hidden-objectives) [AlignmentDec 18, 2024\\
**Alignment faking in large language models** \\
This paper provides the first empirical example of a model engaging in alignment faking without being trained to do so—selectively complying with training objectives while strategically preserving existing preferences.](https://www.anthropic.com/research/alignment-faking) [AlignmentJun 17, 2024\\
**Sycophancy to subterfuge: Investigating reward tampering in language models** \\
Can minor specification gaming evolve into more dangerous behaviors? This paper

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