title: Reasoning Like an Economist: Post-Training on Economic Problems Induces
    Strategic Generalization in LLMs
summary: 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 .
paper_authors._id: 683e646de9f216ff5a3e5dea
paper_authors.name: Yufa Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:46:13.058Z
paper_authors._id: 658ab894c4b2004663dff3ae
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/658ab894c4b2004663dff3ae/oPRnFuW2Imaa2KkYWNbSf.jpeg
paper_authors.fullname: YUFA ZHOU
paper_authors.type: user
paper_authors.user: MasterZhou
paper_authors._id: 683e646de9f216ff5a3e5deb
paper_authors.name: Shaobo Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:46:16.609Z
paper_authors._id: 66968099c952e09a4cb29f78
paper_authors.avatarUrl: /avatars/bd3a361fe5315e26e9ae328071704eed.svg
paper_authors.fullname: Wang
paper_authors.type: user
paper_authors.user: Steven-Shaobo
paper_authors._id: 683e646de9f216ff5a3e5dec
paper_authors.name: Xingyu Dong
paper_authors._id: 683e646de9f216ff5a3e5ded
paper_authors.name: Xiangqi Jin
paper_authors._id: 683e646de9f216ff5a3e5dee
paper_authors.name: Yifang Chen
paper_authors._id: 683e646de9f216ff5a3e5def
paper_authors.name: Yue Min
paper_authors._id: 683e646de9f216ff5a3e5df0
paper_authors.name: Kexin Yang
paper_authors._id: 683e646de9f216ff5a3e5df1
paper_authors.name: Xingzhang Ren
paper_authors._id: 683e646de9f216ff5a3e5df2
paper_authors.name: Dayiheng Liu
paper_authors._id: 683e646de9f216ff5a3e5df3
paper_authors.name: Linfeng Zhang
paper_title: Reasoning Like an Economist: Post-Training on Economic Problems Induces
    Strategic Generalization in LLMs
paper_summary: 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 .
paper_ai_summary: Post-training techniques such as Supervised Fine-Tuning and Reinforcement Learning with Verifiable Rewards improve the reasoning and economic rationality of Large Language Models in multi-agent scenarios through domain-aligned training.
paper_ai_keywords: Large Language Models Multi-Agent Systems Supervised Fine-Tuning Reinforcement Learning with Verifiable Rewards economic reasoning domain-aligned post-training