UCSD 论文将心智理论融入 7B 智能体训练奖励
UC San Diego 论文提出在 7B 智能体的 RL 训练奖励中内置心智理论，让智能体学会推断伙伴的信念、意图与想法（含伙伴对自身的看法），部署时无需额外模型即可独立运行。
A new UC San Diego paper builds theory of mind into a 7B agent through its training reward, so the agent learns to read a partner’s beliefs and goals and then runs on its own with no extra model at deployment.
LLM agents that negotiate, cooperate or role-play usually guess the other side's goals from prompt text or memory summaries. They keep no explicit estimate of the partner's knowledge or plans, so they can misread intent.
They train a model that infers the partner's beliefs, intents and thoughts, including what the partner thinks of the agent. That model scores candidate replies during RL, and the final agent runs alone without it.
For agents that negotiate or collaborate, train with a reward that judges replies against an inferred model of the partner's goals and beliefs.
– arxiv. org/abs/2610.12453
Title: "Mental-Models for Multi-Agent Systems"
来源：rohanpaul_ai · x.com