Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual | ScienceToStartup | ScienceToStartup

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References (29)

[1]

Alignment of large language models with constrained learning

2025Botong Zhang, Shuo Li et al.

[2]

SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety

2025Geon-hyeong Kim, Youngsoo Jang et al.

[3]

Multi-Objective Preference Optimization: Improving Human Alignment of Generative Models

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"A theoretical framework for stabilizing safe RLHF algorithms in large language models using optimistic primal-dual methods."

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Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual

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References (29)

Founder's Pitch

"A theoretical framework for stabilizing safe RLHF algorithms in large language models using optimistic primal-dual methods."

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Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual

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References (29)

Founder's Pitch

"A theoretical framework for stabilizing safe RLHF algorithms in large language models using optimistic primal-dual methods."

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