In our evaluation of DPO's efficiency in text generation, we observed that it strikes an effective balance between maximizing rewards and minimizing KL-divergence, outperforming traditional algorithms like PPO.
In larger model contexts, DPO demonstrated competitive performance on challenging RLHF tasks, including summarization and dialogue generation, often requiring minimal hyperparameter tuning to achieve results on par with the best of N sampled trajectories.
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