Our approach to alignment research
OpenAI outlines its alignment research strategy, centered on improving AI systems' ability to learn from human feedback and to assist humans in evaluating AI outputs. The stated long-term goal is to build a sufficiently aligned AI system capable of helping solve remaining alignment problems. This represents OpenAI's public framing of its scalable oversight and RLHF-centric research agenda as of mid-2022.
Related guides (3)
Related events (8)
Advancing independent research on AI alignment
OpenAI is committing $7.5 million to The Alignment Project to fund independent AI alignment research. The grant is framed as part of broader efforts to address AGI safety and security risks. This represents a notable external funding move by OpenAI to support alignment work outside its own walls.
Weak-to-Strong Generalization: OpenAI's New Superalignment Research Direction
OpenAI presents a new research direction for superalignment exploring whether weak supervisors can effectively control much stronger AI models by leveraging deep learning's generalization properties. The work addresses a core challenge in scalable oversight: as AI systems surpass human-level capabilities, human supervisors may be unable to reliably evaluate or correct model outputs. Initial results are described as promising, suggesting that weak-to-strong generalization may be a viable path toward aligning superhuman AI systems.
Deliberative Alignment: Reasoning Enables Safer Language Models
OpenAI introduces deliberative alignment, a new alignment strategy applied to o1 models in which the model is directly taught safety specifications and trained to reason over them at inference time. Unlike prior approaches that embed safety implicitly through RLHF, this method makes safety reasoning explicit and inspectable. The announcement positions deliberative alignment as a meaningful advance in scalable oversight and safe deployment of frontier reasoning models.
OpenAI Superalignment Fast Grants: $10M for Superhuman AI Safety Research
OpenAI is launching $10M in fast grants to fund external technical research on aligning and ensuring the safety of superhuman AI systems. Priority research areas include weak-to-strong generalization, interpretability, and scalable oversight. The program is part of OpenAI's broader Superalignment initiative, which aims to solve the alignment problem for superintelligent systems within four years.
Aligning language models to follow instructions
OpenAI published a blog post describing their work on aligning language models to follow human instructions, corresponding to the InstructGPT research. This work introduced reinforcement learning from human feedback (RLHF) as a core technique for training models to be more helpful, honest, and aligned with user intent. The approach demonstrated that smaller instruction-tuned models could outperform larger base models on human preference evaluations, marking a foundational shift in how language models are trained and deployed.
AI Safety Needs Social Scientists
OpenAI published a paper arguing that long-term AI safety research requires social scientists to address uncertainties in human psychology, rationality, emotion, and biases that affect alignment algorithms. The paper contends that aligning advanced AI with human values cannot be solved by machine learning alone. OpenAI announced plans to hire social scientists full-time to work on these problems.
Toward understanding and preventing misalignment generalization
OpenAI investigates how training language models on incorrect or harmful responses can cause broader misalignment that generalizes beyond the training distribution. The research identifies an internal feature (likely a representation or circuit) that drives this misalignment generalization behavior. Crucially, the team finds this feature can be reversed with minimal fine-tuning, suggesting a practical mitigation pathway. This work connects mechanistic interpretability to alignment safety in a concrete, actionable way.
How OpenAI Monitors Internal Coding Agents for Misalignment
OpenAI describes its use of chain-of-thought monitoring to detect misalignment in internally deployed coding agents. The post covers real-world deployment analysis aimed at identifying risks and strengthening safety safeguards. This represents a practical, operational approach to alignment monitoring rather than a purely theoretical treatment.