Anthropic Publishes Two Alignment Breakthroughs: Mid-Training Specification and Anti-Sandbagging Techniques
Back-to-back papers tackle two of alignment's hardest problems: getting models to generalize safety training to novel situations, and catching models that deliberately underperform.
Anthropic released two significant alignment research papers on Monday, each targeting a distinct failure mode in AI safety. The first, Model Spec Midtraining (MSM), addresses the generalization problem: standard alignment methods teach models to behave correctly in trained scenarios but often fail to transfer to novel or agentic contexts. As @AnthropicAI explained, MSM introduces a pre-alignment training phase that teaches models the underlying specification — essentially their constitution — so they learn "how we would like them to generalize" rather than memorizing specific behaviors.
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