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cs.LG2025
MergeBench: A Benchmark for Merging Domain-Specialized LLMs
Yifei He, Siqi Zeng, Yuzheng Hu +3
Model merging provides a scalable alternative to multi-task training by combining specialized finetuned models through parameter arithmetic, enabling efficient deployment without t…
cs.LG2024★ 1 cited
Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
Shuang Qiu, Dake Zhang, Rui Yang +2
This paper investigates multi-objective reinforcement learning (MORL), which focuses on learning Pareto optimal policies in the presence of multiple reward functions. Despite MORL'…
cs.LG2024
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards
Haoxiang Wang, Yong Lin, Wei Xiong +5
Fine-grained control over large language models (LLMs) remains a significant challenge, hindering their adaptability to diverse user needs. While Reinforcement Learning from Human…