D-JEPA checkpoints

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Task/module profiles for D-JEPA: A Decision-Aligned Latent World Model. See each profile's README.md and config.json for architecture, dependencies, model settings and loading instructions.

Choose a checkpoint profile

Task	Profile	Contents
PushT	Relational alignment	Learned decision-alignment module
PushT	Predictive adaptation	Updated parameters; requires the exact base model
PushT	Adapted predictor	Full predictor weights
PushT	Multi-geometry alignment	Module using four predictive geometries
PushT	Exact representation realization	Two predictors and relation module in one tensor file
Reacher	Relational alignment	Task-specific decision-alignment module
Reacher	Temporal transport	Bounded future-update module
Reacher	Full model	Predictors, alignment and transport weights
Granular	Relational alignment	Spatial-feature alignment module
Granular	Multi-view alignment	Four-view variant using the same backbone
PushObj	Unseen-shape transfer	Task-local ordinal alignment module
PushT	Visual-shift alignment	Corruption-trained ordinal alignment module

These are configurations of D-JEPA, not 12 separate methods. Each profile contains model.pt, config.json and its own card.

Loading and dependencies

Download an individual checkpoint profile with the Hugging Face CLI:

hf download Shuaijun/D-JEPA \
  pusht-relational/model.pt pusht-relational/config.json pusht-relational/README.md \
  --local-dir checkpoints/D-JEPA

This repository hosts custom PyTorch checkpoint profiles, not dataset splits. It does not require the Dataset Viewer. The linked supervision dataset is a downloadable ZIP/NPZ archive; see its card for extraction and loading instructions.

All model.pt files contain tensor dictionaries and support torch.load(path, map_location="cpu", weights_only=True). The trained parameters are unchanged; optimizer state is omitted.

import torch

state = torch.load("checkpoints/D-JEPA/pusht-relational/model.pt",
                   map_location="cpu", weights_only=True)

This reads a tensor state dictionary, not an instantiated model. Use the matching D-JEPA architecture and configuration; these custom modules are not loaded through Transformers AutoModel.from_pretrained.

Profiles with upstream_included: true contain pretrained predictors. Other profiles contain our learned modules or updated parameter subsets and require their recorded predictive models/features. pusht-exact-realization-full is one file containing two predictors and a relational operator, not a distilled single-backbone student.

Reacher's physical result uses the relational selector; temporal transport is also provided but its native-distance diagnostic is not a substitute result. Multi-geometry's 128-start development result remains a separate protocol from the independent 256-start PushT evaluation.

Local release candidate: authors have not yet selected publication licenses or completed all upstream weight redistribution checks. This card grants no new rights to third-party material. The author-designated repository is Shuaijun/D-JEPA.