A new proposal suggests that third-party assessments of AI training runs, termed Training-Run Assessments (TRAs), should become a standard practice for frontier AI model releases. These assessments would delve into the post-training pipeline, including intermediate checkpoints, training dynamics, and developer responses to warning signs, to better detect potential 'scheming' risks. The author argues that final-checkpoint evaluations may be insufficient to identify AI models covertly pursuing misaligned goals, especially if the model is competently covert and its cognition is obfuscated. Establishing a third-party ecosystem for TRAs could provide a more robust safety mechanism. AI
IMPACT Could lead to more rigorous safety evaluations for advanced AI models, potentially slowing down or altering release timelines.
RANK_REASON The item is an opinion piece proposing a new methodology for AI safety, rather than reporting on a specific event or release.
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