Research acceleration: The view inside OpenAI
Inside OpenAI, coding agents are reshaping AI research. Explore early data on agent usage, experiment velocity, task complexity, and research acceleration.
Research and Releases
Informal updates from the OpenAI team
An Alien Mind
Jakub Pachocki reflects on increasingly capable AI and the challenge of keeping it aligned. He calls for stronger safeguards and international coordination.
The Hugging Face incident and the road ahead
Findings from the incident and the response across model security, monitoring, and alignment.
Measuring Reward-Seeking by Instilling Contrastive Beliefs
Testing whether behavior changes when a model believes its grader wants something different.
Reinforcement learning towards broadly and persistently beneficial models
Testing how alignment improvements hold across domains and under adversarial pressure.
Can public chat data predict real-world AI misalignments?
Comparing public evaluation signals with evidence from deployment.
Investigating the consequences of accidentally grading CoT during RL
An investigation of affected reward pathways and possible effects on monitorability.
Auto-review of agent actions without synchronous human oversight
Using a separate agent to review proposed actions that cross a boundary.
Open Sourcing Monitorability Evaluations
Datasets, code, and an evaluation filtering strategy for studying monitorability.
Introducing the OpenAI Safety Fellowship
A program supporting independent alignment and safety research.
How far does alignment midtraining generalize?
Experiments spanning midtraining, posttraining, and generalization.
Introducing Model Spec Evals
Evaluating how well models follow the OpenAI Model Spec.
Training agents to self-report misbehavior
Training agents to report covert misbehavior through a dedicated tool.
How we monitor internal coding agents for misalignment
Monitoring model behavior in real internal workflows.
Metagaming matters for training, evaluation, and oversight
Metagaming can complicate how we interpret behavior, and current models still give us a chance to study it directly.
Interpreting Black Box Reward Models
ARGO distills black-box reward models into interpretable rubrics using reinforcement learning.
Reasoning models struggle to control their chains of thought, and that’s good
Why a limitation of frontier models is reassuring for AI safety.
Advancing independent research on AI alignment
We’re committing $7.5M to The Alignment Project to fund independent research developing mitigations to safety and security risks from misaligned AI.
Discovering unknown AI misalignments in real-world usage
Reasoning models can find and understand unknown misaligned behaviors from how users respond.
CoVal: Learning values-aware rubrics from the crowd
An experimental dataset of crowd-written rubrics that surfaces why people prefer one model output over another.
Why we are excited about confessions
Deeper analysis of confession training and comparisons to chain-of-thought monitoring.
Helpful assistant features suppress emergent misalignment
Emergent misalignment not only activates misaligned personas, but also suppresses helpful assistant personas.
Evaluating chain-of-thought monitorability
We introduce evaluations for chain-of-thought monitorability and study how it scales with test-time compute, reinforcement learning, and pretraining.
Sidestepping Evaluation Awareness and Anticipating Misalignment with Production Evaluations
A pipeline to uncover unknown misaligned behavior and scale the creation of realistic evaluations.
Debugging misaligned completions with sparse-autoencoder latent attribution
Efficiently finding features that cause behaviors.
A Practical Approach to Verifying Code at Scale
We train and deploy an AI review agent optimised for precision and real-world use, enabling oversight to scale with autonomous code generation.
Hello World
Introducing our blog on alignment research.