metadata
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:
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
@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},
}