Deal or No Deal? End-to-End Learning for Negotiation Dialogues
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Cited by 43 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Much of human dialogue occurs in semi-cooperative settings, where agents with different goals attempt to agree on common decisions. Negotiations require complex communication and reasoning skills, but success is easy to measure, making this an interesting task for AI. We gather a large dataset of human-human negotiations on a multi-issue bargaining task, where agents who cannot observe each other's reward functions must reach an agreement (or a deal) via natural language dialogue. For the first time, we show it is possible to train end-to-end models for negotiation, which must learn both linguistic and reasoning skills with no annotated dialogue states. We also introduce dialogue rollouts, in which the model plans ahead by simulating possible complete continuations of the conversation, and find that this technique dramatically improves performance. Our code and dataset are publicly available (https://github.com/facebookresearch/end-to-end-negotiator).
Key facts. Cited by 209 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Jun 2017.
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Citing papers per year: 2021: 26; 2022: 23; 2023: 34; 2024: 43; 2025: 32; 2026: 51.
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- 2026CallBench: A Benchmark for Dual-Goal Coordination in Phone Call Assistants22 Jun 2026
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- 2026Distilling Bayesian Belief States into Language Models for Auditable Negotiation6 May 2026
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