Buchumschlag

CWM: An Open-Weights LLM for Research on Code Generation with World Models

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	FAIR CodeGen team, Copet, Jade, Carbonneaux, Quentin, Cohen, Gal, Gehring, Jonas, Kahn, Jacob, Kossen, Jannik, Kreuk, Felix, McMilin, Emily, Meyer, Michel, Wei, Yuxiang, Zhang, David, Zheng, Kunhao, Armengol-Estapé, Jordi, Bashiri, Pedram, Beck, Maximilian, Chambon, Pierre, Charnalia, Abhishek, Cummins, Chris, Decugis, Juliette, Fisches, Zacharias V., Fleuret, François, Gloeckle, Fabian, Gu, Alex, Hassid, Michael, Haziza, Daniel, Idrissi, Badr Youbi, Keller, Christian, Kindi, Rahul, Leather, Hugh, Maimon, Gallil, Markosyan, Aram, Massa, Francisco, Mazaré, Pierre-Emmanuel, Mella, Vegard, Murray, Naila, Muzumdar, Keyur, O'Hearn, Peter, Pagliardini, Matteo, Pedchenko, Dmitrii, Remez, Tal, Seeker, Volker, Selvi, Marco, Sultan, Oren, Wang, Sida, Wehrstedt, Luca, Yoran, Ori, Zhang, Lingming, Cohen, Taco, Adi, Yossi, Synnaeve, Gabriel
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Software Engineering Artificial Intelligence Machine Learning 68T07 I.2.7
Online-Zugang:	Acceder al recurso
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Online

https://arxiv.org/abs/2510.02387