Author

Sangdon Park

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#machine learning Preprint Oct 2026

Which Preferences to Train On? End-to-End Multi-Objective Alignment with an Adversarial Preference Distribution

Aligning large language models (LLMs) with human values is important for safe, efficient, and beneficial AI deployment. However, human values are multifaceted: helpfulness, harmlessness and humor trade off against one another, and different users want different trade-offs. Multi-objective alignment (MOA) addresses this...

Minjae Lee, Kyunghyun Cho, Sangdon Park · 0 citations

#machine learning Jun 2026

The Interplay of Harness Design and Post-Training in LLM Agents

It is shown that harness-aware post-training not only improves in-distribution performance but also enables agents to robustly adapt to OOD settings, highlighting the importance of harness-aware post-training under such shifts.

Kyungmin Kim, Youngbin Choi, Seoyeon Lee et al. · arXiv.org · 7 citations

Jul 2026

MJ: Multi-turn LLM Jailbreaking via Decomposed Credit Assignment

Modern large language models (LLMs) operate in interactive multi-turn settings, making multi-turn jailbreaking a realistic threat model and an important setting for automated red teaming. A core challenge in learning multi-turn jailbreak attackers is credit assignment: different turns contribute differently to the fina...

Junyoung Park, N. Park, Sechan Lee et al. · arXiv.org · 0 citations