| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - jepa |
| - world-models |
| - model-based-rl |
| - planning |
| - arxiv:2607.04978 |
| --- |
| |
| # Qantara headline checkpoints |
|
|
| Released checkpoints for **Qantara**, a compact (~21M-param) JEPA world model for |
| goal-conditioned planning from pixels and actions, and for our **LeWM** reproduction |
| baseline. These reproduce the LeWM-suite headline numbers (Table 1) from the ICML 2026 |
| workshop paper. |
|
|
| - **Project page:** https://corl-team.github.io/qantara |
| - **Code:** https://github.com/corl-team/qantara |
| - **Paper:** https://arxiv.org/abs/2607.04978 |
| - **Thread:** https://x.com/rusrakhimov/status/2074847486288806306 |
|
|
| ## Files |
|
|
| 24 checkpoints = 2 methods × 4 environments × 3 training seeds. |
|
|
| | Pattern | Method | |
| |---|---| |
| | `qantara-<env>-s<seed>.ckpt` | Qantara (γ=1, λ_z=3, nulldrop=0) headline | |
| | `lewm-<env>-s<seed>.ckpt` | LeWM reproduction baseline | |
| |
| `env ∈ {pusht, tworoom, cube, reacher}`, `seed ∈ {11, 22, 33}`. |
| |
| ## Loading |
| |
| Each file is a full model object (`torch.save` of a `jepa.JEPA`). Clone the code repo, |
| then: |
| |
| ```python |
| import torch |
| model = torch.load("qantara-pusht-s11.ckpt", map_location="cpu", weights_only=False) |
| ``` |
| |
| The repository's `eval.py` consumes these directly. See the repo README for the full |
| train → eval → figure pipeline. |
| |
| ## Citation |
| |
| ```bibtex |
| @inproceedings{qantara2026, |
| title = {Qantara: Bridge-Flow Training for Multi-Paradigm JEPA Control}, |
| author = {Rakhimov, Ruslan and Bredis, George and Maksyuta, Yuriy and Gavrilov, Daniil}, |
| booktitle = {ICML 2026 Workshop on Decision-Making from Offline Datasets to Online Adaptation: Black-Box Optimization to Reinforcement Learning}, |
| year = {2026}, |
| url = {https://arxiv.org/abs/2607.04978}, |
| } |
| ``` |
| |
