CWM: An Open-Weights LLM for Research on Code Generation with World
  Models
Authors: name: FAIR CodeGen team; name: Quentin Carbonneaux; name: Gal Cohen; name: Jonas Gehring; name: Jacob Kahn; name: Jannik Kossen; name: Felix Kreuk; name: Emily McMilin; name: Michel Meyer; name: Yuxiang Wei; name: David Zhang; name: Kunhao Zheng; name: Jordi Armengol-Estapé; name: Pedram Bashiri; name: Maximilian Beck; name: Pierre Chambon; name: Abhishek Charnalia; name: Chris Cummins; name: Juliette Decugis; name: Zacharias V. Fisches; name: François Fleuret; name: Fabian Gloeckle; name: Alex Gu; name: Michael Hassid; name: Daniel Haziza; name: Badr Youbi Idrissi; name: Christian Keller; name: Rahul Kindi; name: Hugh Leather; name: Gallil Maimon; name: Aram Markosyan; name: Francisco Massa; name: Pierre-Emmanuel Mazaré; name: Vegard Mella; name: Naila Murray; name: Keyur Muzumdar; name: Peter O'Hearn; name: Matteo Pagliardini; name: Dmitrii Pedchenko; name: Tal Remez; name: Volker Seeker; name: Marco Selvi; name: Oren Sultan; name: Sida Wang; name: Luca Wehrstedt; name: Ori Yoran; name: Lingming Zhang; name: Taco Cohen; name: Yossi Adi; name: Gabriel Synnaeve
Published: 2025-09-30 21:47:10

Abstract: We release Code World Model (CWM), a 32-billion-parameter open-weights LLM,
to advance research on code generation with world models. To improve code
understanding beyond what can be learned from training on static code alone, we
mid-train CWM on a large amount of observation-action trajectories from Python
interpreter and agentic Docker environments, and perform extensive multi-task
reasoning RL in verifiable coding, math, and multi-turn software engineering
environments. With CWM, we provide a strong testbed for researchers to explore
the opportunities world modeling affords for improving code generation with
reasoning and planning in computational environments. We present first steps of
how world models can benefit agentic coding, enable step-by-step simulation of
Python code execution, and show early results of how reasoning can benefit from
the latter. CWM is a dense, decoder-only LLM trained with a context size of up
to 131k tokens. Independent of its world modeling capabilities, CWM offers
strong performance on general coding and math tasks: it reaches pass@1 scores
of 65.8% on SWE-bench Verified (with test-time scaling), 68.6% on
LiveCodeBench, 96.6% on Math-500, and 76.0% on AIME 2024. To support further
research on code world modeling, we release model checkpoints after
mid-training, SFT, and RL.