Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations

• URL: http://arxiv.org/abs/2607.05863v1
• Date: Tue, 07 Jul 2026 05:41:54 GMT
• Title: Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations
• Abstract summary: Negotiation is a fundamental strategic interaction in management science.<n>This paper proposes a specialized training recipe using Reinforcement Learning from Verifiable Rewards.
• Score: 22.41117839056233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Negotiation is a fundamental strategic interaction in management science, characterized by agents attempting to reach agreements while protecting private information, such as reservation costs and hidden valuations. A prevalent yet complex scenario involves a single seller negotiating concurrently with multiple buyers, each possessing heterogeneous, private budgets. In such settings, constrained by a limited number of communication turns, the seller must balance exploring the broader market to discover the highest valuation with concentrating sufficient turns on a single target buyer to secure the best possible outcome. Our analysis reveals a significant gap in standard Large Language Models (LLMs): while these models are linguistically proficient, they fail to act as effective economic decision-makers. Specifically, they exhibit a failure to explore the buyer pool, often fixating on the current highest bid rather than strategically investigating the market to discover latent high valuations. In this paper, we propose a specialized training recipe using Reinforcement Learning from Verifiable Rewards (RLVR). By anchoring the reward function to objective economic outcomes, the strategic balance between market discovery and surplus extraction emerges natively through the learning process. Our results demonstrate that the trained seller undergoes a multi-stage strategic evolution, learning to leverage price anchoring and strategic probing to identify more profitable counterparties. The agent extracts a substantially higher surplus than frontier models by both improving its persuasive bargaining skills and consistently closing deals with high-value buyers. Finally, we show that our seller strategies generalize robustly to unseen buyer negotiation styles and budget distributions.

Related papers

• Business Arena: Benchmarking LLM Agents in a Realistic Marketplace [14.641921772947926]
  Business Arena is a controlled environment where an AI agent runs a cross-border shop, buying from suppliers and selling to buyers over a long horizon.<n>We evaluate 15 frontier models and find a ninefold difference in mean final net worth.<n>Even the best model falls behind human-designed strategies, indicating that business operation remains challenging for LLM agents.
  arXiv  Detail & Related papers  (2026-08-09T10:08:21Z)
• Cattle Trade: A Multi-Agent Benchmark for LLM Bluffing, Bidding, and Bargaining [0.5691230599672109]
  We introduce textscCattle Trade, a benchmark for evaluating large language models (LLMs) as agents in strategic reasoning.<n>The benchmark combines auctions, hidden-offer trade challenges (TCs), bargaining, bluffing, opponent modeling, and resource allocation within a single long-horizon game.<n>We evaluate seven cost-efficient language models and three deterministic code agents across 242 games.
  arXiv  Detail & Related papers  (2026-05-14T08:20:03Z)
• Instructing LLMs to Negotiate using Reinforcement Learning with Verifiable Rewards [45.56436052535799]
  We investigate if Reinforcement Learning from Verifiable Rewards can effectively teach Large Language Models to negotiate.<n>We introduce a framework that trains a mid-sized buyer agent against a regulated seller across a wide distribution of real-world products.<n>Our results demonstrate that this verifiable training allows a 30B agent to significantly outperform frontier models over ten times its size in extracting surplus.
  arXiv  Detail & Related papers  (2026-04-10T19:35:39Z)
• Training Language Models for Bilateral Trade with Private Information [47.25254982913256]
  Bilateral trade demands individual, strategic surplus rationality, and cooperation to realize gains from trade.<n>We develop a structured bargaining environment where large language models negotiate via tool calls within an event-driven simulator.<n>The environment serves two purposes: a benchmark for frontier models and as a training environment for open-weight models via reinforcement learning.
  arXiv  Detail & Related papers  (2026-04-10T03:04:20Z)
• ChatShopBuddy: Towards Reliable Conversational Shopping Agents via Reinforcement Learning [88.87305027895657]
  This work investigates Reinforcement Learning-based optimization for shopping agents in real-world scenarios.<n>Agents must simultaneously satisfy multiple interdependent objectives spanning objective metrics (product correctness), subjective qualities (persuasiveness), outcome rewards (final response quality), and process rewards (tool efficiency)<n>Experiments demonstrate that our RL-trained agent, ChatShopBuddy, consistently outperforms larger models relying on generic reasoning.
  arXiv  Detail & Related papers  (2026-03-06T09:18:51Z)
• PieArena: Frontier Language Agents Achieve MBA-Level Negotiation Performance and Reveal Novel Behavioral Differences [13.759960839511807]
  We introduce PieArena, a large-scale negotiation benchmark grounded in multi-agent interactions.<n>We find systematic evidence of human-expert-level performance in which a representative frontier language agent (GPT-5) matches or outperforms trained business-school students.
  arXiv  Detail & Related papers  (2026-02-05T04:52:20Z)
• The Invisible Handshake: Tacit Collusion between Adaptive Market Agents [16.94262518248427]
  We study the emergence of tacit collusion between adaptive trading agents in a market with endogenous price formation.<n>We show that, when agents follow simple learning algorithms to maximize their own wealth, the resulting dynamics converge to collusive strategy profiles.
  arXiv  Detail & Related papers  (2025-10-14T08:28:33Z)
• Learning to Lead: Incentivizing Strategic Agents in the Dark [50.93875404941184]
  We study an online learning version of the generalized principal-agent model.<n>We develop the first provably sample-efficient algorithm for this challenging setting.<n>We establish a near optimal $tildeO(sqrtT) $ regret bound for learning the principal's optimal policy.
  arXiv  Detail & Related papers  (2025-06-10T04:25:04Z)
• Agents Require Metacognitive and Strategic Reasoning to Succeed in the Coming Labor Markets [53.0367886783772]
  Labor markets are affected by the economic forces of adverse selection, moral hazard, and reputation.<n>Agents must use metacognitive and strategic reasoning to perform effectively.
  arXiv  Detail & Related papers  (2025-05-26T15:22:04Z)
• Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
  This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective. We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
  arXiv  Detail & Related papers  (2021-12-08T14:55:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.