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Adversarial training with human demonstrations can significantly enhance the quality and diversity of language model outputs while preserving accuracy.
Fixed counterfactual explanations can lead LMs to generate more accurate introspections about their behaviors, even as those behaviors change over time.
Executable programs can now replace attention heads in transformers with minimal performance loss, achieving over 75% similarity to original patterns.
Retaining visual figures in skill artifacts boosts CUA performance by over 23 points, proving that seeing is believing in agent training.
