CoLLM-CC is a Multi-Agent Actor-Critic (MAAC) approach designed to optimize decentralized collaboration among Large Language Models (LLMs). It employs a centralized critic to reduce training variance, proving particularly effective for long-horizon and sparse-reward tasks.
CoLLM-CC is a new method that helps multiple AI language models work together more effectively, especially for complex tasks where it's hard to get immediate feedback. It uses a central 'judge' to guide the models' learning, making the process more stable and reliable than older techniques.
Collaborative LLM with Centralized Critic
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