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cs.AI2024
Coevolving with the Other You: Fine-Tuning LLM with Sequential Cooperative Multi-Agent Reinforcement Learning
Hao Ma, Tianyi Hu, Zhiqiang Pu +4
Reinforcement learning (RL) has emerged as a pivotal technique for fine-tuning large language models (LLMs) on specific tasks. However, prevailing RL fine-tuning methods predominan…
cs.AI2024
Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems
Qingxu Fu, Zhiqiang Pu, Min Chen +2
Large-scale heterogeneous multiagent systems feature various realistic factors in the real world, such as agents with diverse abilities and overall system cost. In comparison to ho…