Multi-Agent Negotiation

Follow this topic Ask about this topic

Momentum

1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 25

All topics

Do Small Language Models Learn to Negotiate? A Controlled Scaling Study of RL-Trained Sellers
Oct 5, 2026Pedro Tabacof, Sagar JoglekarRL for Language Models Multi-Agent Negotiation
Fin AI Research
Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets
Sep 27, 2026Shuze Daniel Liu, Claire Chen, Jiuqi Wang +2Multi-Agent Negotiation Automated Negotiation
Massachusetts Institute of Technology, Cambridge, Massachusetts, USA · Purdue University, West Lafayette, Indiana, USA · California Institute of Technology, Pasadena, California, USA +2
Evaluating Rational Contracting in Natural Language
Aug 11, 2026Bhavyesh Sajja, Max Kleiman-Weiner, Roger Zimmermann +1Multi-Agent Negotiation Automated Negotiation
Department of Computer Science, National University of Singapore · Department of Computer Science, University of Washington · A*STAR Institute of Advanced Intelligence and Computing
Deal Me Maybe: The Role of Emotions in Multi-Agent Negotiation
Aug 7, 2026Massimiliano Luca, Apoorva Singh, Bruno LepriMulti-Agent Negotiation Automated Negotiation
Bruno Kessler Foundation
When LLM Agents Negotiate: Private Information and Dynamic Bargaining in Supply Chains
Jul 29, 2026Chen Liang, Fasheng XuMulti-Agent Negotiation Automated Negotiation
School of Business, University of Connecticut
CoRenew: A large language model agent-based policy simulation platform for multifamily residential redevelopment
Jul 28, 2026Yudi Zhang, Yuming Lin, Li Tian +2Multi-Agent LLM Systems Multi-Agent Negotiation
Department of Urban Planning, Tsinghua University, Beijing, 100084, China · Department of City and Regional Planning, UC Berkeley, Berkeley, CA, 94720, USA · Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China
Commitment To Cooperation With Self-Negotiated Contracts
Jul 23, 2026Tim Wyse, Kaitlin Bustos, Yulia Volkova +1Multi-Agent LLM Systems Multi-Agent Coordination
Algoverse AI Research · University of Washington
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
Jul 16, 2026Dylan Van Mulders, Matthias Bogaert, Dirk Van den PoelMulti-Agent Negotiation Automated Negotiation
Ghent University CVAMO Core Lab
MIND-CAVs: Multi-Intelligence Negotiation and Decision System for CAVs based on Intent-Driven Autonomy
Jul 16, 2026Mainak Mondal, Yihang Feng, Yangchao Luo +1Multi-Agent Coordination Multi-Agent Negotiation
University of Connecticut
Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations
Jul 7, 2026Shuze Daniel Liu, Claire Chen, Jiabao Sean Xiao +2Multi-Agent Negotiation Automated Negotiation
Institute for Data, Systems, and Society, Massachusetts Institute of Technology · Mitch Daniels School of Business, Purdue University · The Division of Physics, Mathematics and Astronomy, California Institute of Technology +3
When Does Personality Composition Matter for Multi-Agent LLM Teams?
Jun 25, 2026Aryan Keluskar, Amrita Bhattacharjee, Huan LiuMulti-Agent LLM Systems Multi-Agent Negotiation
School of Computing & AI Arizona State University Tempe, AZ 85281, USA
SidConArena: An Environment Evaluating Agents in Open-Ended,Positive-Sum Bargaining Game
Jun 24, 2026Yeqi Feng, Yuxin Chen, Tianxing HeMulti-Agent Negotiation Game Theory
Institute for Interdisciplinary Information Sciences, Tsinghua University
Used Car Salesbots? Honesty and Credulity of LLMs as Bargaining Agents under Partial Information
May 29, 2026Antonio Valerio Miceli-Barone, Vaishak Belle, Shay B. CohenMulti-Agent LLM Systems Multi-Agent Negotiation
University of Edinburgh
EmoDistill: Offline Emotion Skill Distillation for Language Model Agents in Adversarial Negotiation
May 26, 2026Yunbo Long, Haolang Zhao, Lukas Beckenbauer +2Multi-Agent Negotiation Automated Negotiation
University of Cambridge · Technical University of Munich · Exiger LLC +1
Counterparty Modeling is Not Strategy: The Limits of LLM Negotiators
May 15, 2026Romain Cosentino, Sarath Shekkizhar, Adam Earle +1Multi-Agent Negotiation Automated Negotiation
Salesforce AI Research
Cattle Trade: A Multi-Agent Benchmark for LLM Bluffing, Bidding, and Bargaining
May 14, 2026Robert Müller, Clemens MüllerMulti-Agent Negotiation Game-Playing Agents
TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate
May 13, 2026Erica Zhang, Fangzhao Zhang, Aneesh Pappu +5Multi-Agent Negotiation Automated Negotiation
♠Stanford School of Engineering · ♣Stanford Department of Economics · ♥Stanford Graduate School of Business +1
LLM-X: A Scalable Negotiation-Oriented Exchange for Communication Among Personal LLM Agents
May 12, 2026Giuliano Lorenzoni, Paulo Alencar, Donald CowanMulti-Agent LLM Systems Multi-Agent Coordination
University of Waterloo · University of Waterloo Waterloo, ON, Canada
Distilling Bayesian Belief States into Language Models for Auditable Negotiation
May 6, 2026Zongqi Cui, Baihan LinMulti-Agent Negotiation Opponent Modeling
Emory University · Icahn School of Medicine at Mount Sinai
Talk is Cheap, Communication is Hard: Dynamic Grounding Failures and Repair in Multi-Agent Negotiation
May 3, 2026Yiheng Yao, Chelsea Zou, Robert D. HawkinsMulti-Agent Coordination Multi-Agent Negotiation
Stanford University
Cooperate to Compete: Strategic Coordination in Multi-Agent Conquest
Apr 28, 2026Abigail O'Neill, Alan Zhu, Mihran Miroyan +2Multi-Agent Coordination Multi-Agent Negotiation
University of California, Berkeley
ArgRE: Formal Argumentation for Conflict Resolution in Multi-Agent Requirements Negotiation
Apr 25, 2026Haowei Cheng, Milhan Kim, Chong Liu +6Requirements Engineering Multi-Agent Negotiation
Department of Computer Science and Communications Engineering, Waseda University, Tokyo, Japan · College of Architecture and Urban Planning, Tongji University, Shanghai, China · Advanced Institute of So-Go-Chi (Convergence Knowledge) Informatics, Tohoku University, Miyagi, Japan +1
Preference Estimation via Opponent Modeling in Multi-Agent Negotiation
Apr 17, 2026Yuta Konishi, Kento Yamamoto, Eisuke Sonomoto +8Multi-Agent Negotiation Opponent Modeling
1Graduate School of Informatics, Kyoto University, Kyoto, Japan · 2Accenture Japan Ltd, Tokyo, Japan
Diversity Without Fidelity: A Solver-Sampler Mismatch in Multi-Agent LLM Negotiation Simulation
Apr 12, 2026Sandro AndricMulti-Agent LLM Systems Multi-Agent Negotiation
The Language of Bargaining: Linguistic Effects in LLM Negotiations
Jan 7, 2026Stuti Sinha, Himanshu Kumar, Aryan Raju Mandapati +2Multilingual Language Model Evaluation Multi-Agent Negotiation
BITS Pilani

Oct 5, 2026, cs.AIJ/K move · Enter open · S save

Do Small Language Models Learn to Negotiate? A Controlled Scaling Study of RL-Trained Sellers

Pedro Tabacof, Sagar Joglekar

Fin AI Research

RL for Language Models Multi-Agent Negotiation Small Language Models Language Model Scaling

Open paper AskPDF arXiv

LLM agents are starting to own the full customer experience. Soon, LLMs may be selling and buying on behalf of companies and customers respectively. Small models are more cost-efficient at scale, but can reinforcement learning train them into competent sellers? We train four Gemma 4 checkpoints (2.3B to 31B effective parameters) with GRPO on a programmatic utility reward for bilateral multi-issue bargaining, and evaluate every arm on the same 1,152 negotiations against two frontier buyers it never saw in training. With the same learning rate (

10^{-6}

) for every size, the gain of the RL model over its base rises from

+0.001

at 2.3B to

+0.078

at 31B. Each size was trained once and the two smallest checkpoints use a different architecture, so we fit no scaling law. Tripling the learning rate, with the same or fewer training steps, improves on the shared rate at every size by

+0.032

(2.3B) to

+0.081

(4.5B). In exploratory comparisons with two frontier models run as sellers, the 12B seller trained at the tripled rate scores above both, though its untrained base already scores as high as they do. The 4.5B seller at that rate shows no detectable difference from either and fits on one 48 GB GPU. A further 2.3B arm at ten times the shared rate raises pooled score, but its gain concentrates on the evaluation buyer that shares a model family with the training pool. These results suggest tuning the learning rate before concluding that a small model cannot learn to negotiate, and testing against buyers from more than one model family.

Momentum

Related topics

Latest papers 25