This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents, and provides insight into their "cognitive" processes. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named roleplaying .

TBAL is emerging as a widely-used solution in practice.

Assistive agents should make humans' lives easier.

Proof of Theorem 4.7 We first prove a Lemma. Theorem A.2. (Theorem 1 in [36]) Let ϵ = max Theorem 4.7 In a two-player game, suppose that According to Theorem A.2, we have J ( π, µ) J ( π, α) E CQL [20] puts regularization on the learning of Q function to penalize out-of-distribution actions. The CSP algorithm is illustrated in Algorithm 1. The proxy model is trained adversarially against our agent, therefore, we set the proxy's reward function to be the negative of our agent's reward. We show experiment details of the Maze example in this section.

While Low-Rank Adaptation (LoRA) has proven beneficial for efficiently fine-tuning large models, LoRA fine-tuned text-to-image diffusion models lack diversity in the generated images, as the model tends to copy data from the observed training samples.