Learning Evolving Latent Strategies for Multi-Agent Language Systems without Model Fine-Tuning
This addresses the challenge of scalable and interpretable strategy learning in multi-agent language systems, offering a low-cost alternative to fine-tuning.
The study tackled the problem of enabling multi-agent language systems to evolve strategies without fine-tuning model parameters by using external latent vectors updated through interaction and reinforcement, resulting in agents developing stable strategic styles and adapting to emotional agents without shared rewards.
This study proposes a multi-agent language framework that enables continual strategy evolution without fine-tuning the language model's parameters. The core idea is to liberate the latent vectors of abstract concepts from traditional static semantic representations, allowing them to be continuously updated through environmental interaction and reinforcement feedback. We construct a dual-loop architecture: the behavior loop adjusts action preferences based on environmental rewards, while the language loop updates the external latent vectors by reflecting on the semantic embeddings of generated text. Together, these mechanisms allow agents to develop stable and disentangled strategic styles over long-horizon multi-round interactions. Experiments show that agents' latent spaces exhibit clear convergence trajectories under reflection-driven updates, along with structured shifts at critical moments. Moreover, the system demonstrates an emergent ability to implicitly infer and continually adapt to emotional agents, even without shared rewards. These results indicate that, without modifying model parameters, an external latent space can provide language agents with a low-cost, scalable, and interpretable form of abstract strategic representation.