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Self-play methods have demonstrated remarkable success in enhancing model capabilities across various domains.
Fine-tuning language models from human preferences. arxiv 2019
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Buy 4 REINFORCE samples, get a baseline for free!, 2019
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Mirror descent policy optimization
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Chen-Yu Wei, Chung-Wei Lee, Mengxiao Zhang, and Haipeng Luo · 2020
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Training a helpful and harmless assistant with reinforcement learning from human feedback
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Finite-time last-iterate convergence for learning in multi-player games
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Self-play fine-tuning converts weak language models to strong language models
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Min-max optimization made simple: Approximating the proximal point method via contraction maps
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Safe rlhf: Safe reinforcement learning from human feedback
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Preference fine-tuning of llms should leverage suboptimal, on-policy data
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Understanding the performance gap between online and offline alignment algorithms
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Gemma: Open models based on gemini research and technology
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Secrets of rlhf in large language models part ii: Reward modeling
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Self-play preference optimization for language model alignment
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A theoretical analysis of nash learning from human feedback under general kl-regularized preference
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Advancing llm reasoning generalists with preference trees
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Token-level direct preference optimization
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Iterative nash policy optimization: Aligning llms with general preferences via no-regret learning
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