[r/ML] [D] Data curation and targeted replacement as a pre-training alignment and controllability method

Summary

This discussion proposes a pre-training AI alignment method focused on data curation and targeted replacement. Instead of post-training interventions like RLHF, it suggests proactively removing or replacing undesirable content, such as violence or deception, from datasets *before* model training. The aim is to prevent models from ever learning from harmful data, offering a foundational approach to controllability.

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Explore related coverage about community news and adjacent AI developments: [r/ML] [D] MYTHOS-INVERSION STRUCTURAL AUDIT, [r/LocalLLaMA] karpathy / autoresearch, [r/ML] [R] Agentic AI and Occupational Displacement: A Multi-Regional Task Exposure Analysis (236 occupations, 5 US metros), [r/ML] Building behavioural response models of public figures using Brain scan data (Predict their next move using psychological modelling) [P].

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