Tie training can make DPO/RLHF-trained AIs generalize better

According to LessWrong, researchers studying how AI systems are trained have found an important problem: methods like DPO and RLHF can teach models to rely on spurious patterns. These are surface-level features that happen to correlate with good behavior in training but don't actually determine true value. When deployed in new situations, those false patterns break down, and the AI starts making poor decisions. The solution proposed is called tie training: exposing models to equally-good action pairs with random labels to help them ignore spurious features. Experiments show that tie training improves how well these systems generalize outside their training distribution.

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