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arXiv Machine Learning By Dereck Piche, Mohammed Muqeeth, Milad Aghajohari, Juan Duque, Michael Noukhovitch, Aaron Courville

Learning Robust Social Strategies with Large Language Models

Read the original on arXiv Machine Learning →

The paper investigates how large language models (LLMs) can be trained to cooperate in multi‑agent social dilemmas. It shows that standard reinforcement learning often leads LLM agents to defect and exploit others, even when they have cooperative priors. By adapting the Advantage Alignment algorithm and introducing a group‑relative baseline, the authors fine‑tune LLMs to achieve higher collective payoffs and resist exploitation, and they present a new Trust‑and‑Split environment that requires natural language communication for optimal outcomes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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