LAC improves LLM decision-making by computing action scores from token logits and combining them with the model's prior policy through a gradient-free KL-constrained update.
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach
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abstract
The remarkable progress in Large Language Models (LLMs) opens up new avenues for addressing planning and decision-making problems in Multi-Agent Systems (MAS). However, as the number of agents increases, the issues of hallucination in LLMs and coordination in MAS have become increasingly prominent. Additionally, the efficient utilization of tokens emerges as a critical consideration when employing LLMs to facilitate the interactions among a substantial number of agents. In this paper, we develop a modular framework called LLaMAC to mitigate these challenges. LLaMAC implements a value distribution encoding similar to that found in the human brain, utilizing internal and external feedback mechanisms to facilitate collaboration and iterative reasoning among its modules. Through evaluations involving system resource allocation and robot grid transportation, we demonstrate the considerable advantages afforded by our proposed approach.
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cs.CL 1years
2025 1verdicts
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Enhancing Decision-Making of Large Language Models via Actor-Critic
LAC improves LLM decision-making by computing action scores from token logits and combining them with the model's prior policy through a gradient-free KL-constrained update.