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Introduction
The game Diplomacy has been a major challenge for artificial intelligence (AI). Unlike other competitive games that AI has recently mastered, such as chess, Go, and poker, Diplomacy cannot be solved purely through self-play; it requires the development of an agent to understand other players’ motivations and perspectives and to use natural language to negotiate complex shared plans. The Meta Fundamental AI Research Diplomacy Team (FAIR) et al. developed an agent that is able to play the full natural language form of the game and demonstrates performance well above the human average in an online Diplomacy league. The present work has far-reaching implications for the development of cooperative AI and language models for communication with people, even when interactions involve a mixture of aligned and competing interests. —YS
Training
Training Code Accessibilitycreative commons (non comm) for model weights, MIT for code https://github.com/facebookresearch/diplomacy_cicero?tab=readme-ov-file#license-for-model-weights
Size Notes: "We obtained a dataset of 125,261 games of Diplomacy played online at webDiplomacy.net. Of these, 40,408 games contained dialogue, with a total of 12,901,662 messages exchanged between players. Player accounts were de-identified and automated redaction of personally identifiable information (PII) was performed by webDiplomacy. We refer to this dataset hereafter as WebDiplomacy ."
Parameters
Notes: "We took R2C2 (22) as our base model – a 2.7B parameter Transformer-based (23) encoder-decoder model pre-trained on text from the Internet using a BART de-noising objective (24)."
Authors
Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyuan Hu, Athul Paul Jacob, Mojtaba Komeili, Karthik Konath, Minae Kwon, Adam Lerer, Mike Lewis, Alexander H. Miller, Sasha Mitts, Adithya Renduchintala, Stephen Roller, Dirk Rowe, Weiyan Shi, Joe Spisak, Alexander Wei, David Wu, Hugh Zhang, Markus Zijlstra
Multimodal
Language
Vision