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Self-evolving large language model (LLM) systems repeatedly propose, evaluate, and incorporate updates to prompts, skills, or other persistent artifacts. Despite their growing effectiveness, these systems typically operate under a predetermined iteration or compute budget, without a principled criterion to determine wh...
This work proposes Distilled Preference Probability Policy Optimization (DP3O), an effective and efficient offline alignment algorithm that outperforms state-of-the-art offline methods, achieves performance comparable to iterative DPO, and reduces training time, demonstrating both its effectiveness and efficiency.
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