Reasoning Like an Economist: Post-Training on Economic Problems Induces
  Strategic Generalization in LLMs
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 .
683e646de9f216ff5a3e5dea
Yufa Zhou
claimed_verified
2025-06-03T08:46:13.058Z
658ab894c4b2004663dff3ae
https://cdn-avatars.huggingface.co/v1/production/uploads/658ab894c4b2004663dff3ae/oPRnFuW2Imaa2KkYWNbSf.jpeg
YUFA ZHOU
user
MasterZhou
683e646de9f216ff5a3e5deb
Shaobo Wang
claimed_verified
2025-06-03T08:46:16.609Z
66968099c952e09a4cb29f78
/avatars/bd3a361fe5315e26e9ae328071704eed.svg
Wang
user
Steven-Shaobo
683e646de9f216ff5a3e5dec
Xingyu Dong
683e646de9f216ff5a3e5ded
Xiangqi Jin
683e646de9f216ff5a3e5dee
Yifang Chen
683e646de9f216ff5a3e5def
Yue Min
683e646de9f216ff5a3e5df0
Kexin Yang
683e646de9f216ff5a3e5df1
Xingzhang Ren
683e646de9f216ff5a3e5df2
Dayiheng Liu
683e646de9f216ff5a3e5df3
Linfeng Zhang
Reasoning Like an Economist: Post-Training on Economic Problems Induces
  Strategic Generalization in LLMs
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 .
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.
Large Language Models Multi-Agent Systems Supervised Fine-Tuning Reinforcement Learning with Verifiable Rewards economic reasoning domain-aligned post-training