Reasoning Like an Economist: Post-Training on Economic Problems Induces
  Strategic Generalization in LLMs
Authors: name: Yufa Zhou, name: Shaobo Wang, name: Xingyu Dong, name: Xiangqi Jin, name: Yifang Chen, name: Yue Min, name: Kexin Yang, name: Xingzhang Ren, name: Dayiheng Liu, name: Linfeng Zhang

Directly training Large Language Models (LLMs) for Multi-Agent Systems (MAS)
remains challenging due to intricate reward modeling, dynamic agent
interactions, and demanding generalization requirements. This paper explores
whether post-training techniques, specifically Supervised Fine-Tuning (SFT) and
Reinforcement Learning with Verifiable Rewards (RLVR), can effectively
generalize to multi-agent scenarios. We use economic reasoning as a
testbed, leveraging its strong foundations in mathematics and game theory, its
demand for structured analytical reasoning, and its relevance to real-world
applications such as market design, resource allocation, and policy analysis.
We introduce Recon (Reasoning like an
ECONomist), a 7B-parameter open-source LLM post-trained on a
hand-curated dataset of 2,100 high-quality economic reasoning problems.
Comprehensive evaluation on economic reasoning benchmarks and multi-agent games
reveals clear improvements in structured reasoning and economic rationality.
These results underscore the promise of domain-aligned post-training for
enhancing reasoning and agent alignment, shedding light on the roles of SFT and
RL in shaping model behavior. Code is available at
https://github.com/MasterZhou1/Recon .