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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},
}
```
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