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Debate training not only curbs reward hacking but also boosts model performance, recovering 45% of lost accuracy compared to traditional RLAIF methods.
Fine-tuning LLMs with RL can boost self-explanation faithfulness from nearly zero to 0.664, revealing surprising cross-intervention generalization capabilities.
