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description= Informal alignment research updates from the OpenAI team.;
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alignment research blog p s the alignment and safety systems teams are hiring alignment research blog informal updates from the openai team 2026 jun 18 reinforcement learning towards broadly and persistently beneficial models training targeting beneficial behavior in realistic scenarios produces broad improvements in alignment that generalize across domains and persist under adversarial pressure jun 16 can public chat data predict real world ai misalignments bridging private deployment evidence and public ai evaluation may 7 investigating the consequences of accidentally grading cot during rl we found limited accidental cot grading in some released models fixed the affected reward pathways and found no clear evidence that monitorability degraded apr 30 auto review of agent actions without synchronous human oversight auto review offers a safer default for deploying coding agents using a separate agent to approve or deny boundary crossing actions apr 23 open sourcing monitorability evaluations we open source datasets and code from our monitoring monitorability paper and share a new filtering strategy for noise dominated intervention evaluation instances apr 6 introducing the openai safety fellowship a pilot program to support independent safety and alignment research and develop the next generation of talent mar 27 how far does alignment midtraining generalize preliminary experiments on alignment and misalignment midtraining reasoning posttraining and generalization to chat and agentic evals mar 25 introducing model spec evals a new evaluation suite for measuring how well models follow the openai model spec mar 21 training agents to self report misbehavior we train agents to call a reporting tool when they covertly misbehave sharply reducing undetected attacks mar 19 how we monitor internal coding agents for misalignment a look at how we monitor internal coding agents to detect misaligned behavior early mar 16 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 mar 11 interpreting black box reward models argo distills black box reward models into interpretable rubrics using reinforcement learning feb 6 discovering unknown ai misalignments in real world usage reasoning models can find and understand unknown misaligned behaviors from how users respond jan 14 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 jan 14 why we are excited about confessions deeper analysis of confession training and comparisons to chain of thought monitoring 2025 dec 22 helpful assistant features suppress emergent misalignment emergent misalignment not only activates misaligned personas but also suppresses helpful assistant personas dec 18 sidestepping evaluation awareness and anticipating misalignment with production evaluations a pipeline to uncover unknown misaligned behavior and scale the creation of realistic evaluations dec 1 debugging misaligned completions with sparse autoencoder latent attribution efficiently finding features that cause behaviors dec 1 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 dec 1 hello world introducing our blog on alignment research newer older
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