1202kbs/FAR-Checkpoints

FAR checkpoints

Checkpoint bundles for FAR, a latent-diffusion world model with a learned, action-conditioned retrieval memory, and its baselines, on the LoopNav, SoundSpaces and AI2-THOR corpora.

• Paper: Learning What to Recall: Adaptive Multi-Cue Episodic Memory for World Models
• Code: https://github.com/sony/far
• Project page: https://1202kbs.github.io/FAR-Project-Page/

Layout

The repo mirrors the code's models/ directory, so a download lands exactly where the configs, launchers and notebooks look:

<corpus>/<arm>/config.yaml                  # rebuilds the model (Hydra)
<corpus>/<arm>/checkpoints/<step>.pth.tar   # EMA generator weights + memory state + step (inference only)
oasis_500m_vit_vae.pth                      # ViT-VAE tokenizer for LoopNav latents
agent_cell_detector.pt                      # latent agent-cell detector for the AI2-THOR-dyn probe
bundles.json                                # this listing with sizes and SHA-256

Bundles hold what inference needs (EMA generator, the trained or frozen memory state, the training step) and cannot resume training. The SDXL VAE used for the SoundSpaces and AI2-THOR corpora is fetched from madebyollin/sdxl-vae-fp16-fix automatically.

Download

python scripts/download_release.py                       # everything, ~12 GB
python scripts/download_release.py --corpus ai2thor_v3   # one corpus
python scripts/download_release.py --arm loopnav/far_multicue

or huggingface-cli download 1202kbs/FAR-Checkpoints --local-dir models.

Bundles

Corpus	Bundle	Arm	Step	Size
AI2-THOR-dyn	ai2thor_dyn/far_meta	FAR -- Meta	500k	0.56 GB
AI2-THOR-dyn	ai2thor_dyn/far_multicue	FAR -- Multi-Cue	500k	0.56 GB
AI2-THOR-dyn	ai2thor_dyn/temporal	Temporal	500k	0.49 GB
AI2-THOR-dyn	ai2thor_dyn/worldmem	WorldMem	500k	0.49 GB
AI2-THOR v3	ai2thor_v3/far_multicue	FAR -- Multi-Cue	700k	0.55 GB
AI2-THOR v3	ai2thor_v3/temporal	Temporal	700k	0.49 GB
AI2-THOR v3	ai2thor_v3/worldmem	WorldMem	700k	0.49 GB
LoopNav	loopnav/far_meta	FAR -- Meta	700k	0.55 GB
LoopNav	loopnav/far_multicue	FAR -- Multi-Cue	700k	0.55 GB
LoopNav	loopnav/far_visual	FAR -- Visual	700k	0.55 GB
LoopNav	loopnav/longlive_rag	LongLive-RAG	700k	0.55 GB
LoopNav	loopnav/temporal	Temporal	700k	0.49 GB
LoopNav	loopnav/worldmem	WorldMem	700k	0.49 GB
SoundSpaces v1	soundspaces_v1/far_meta	FAR -- Meta	700k	0.55 GB
SoundSpaces v1	soundspaces_v1/far_multicue	FAR -- Multi-Cue	700k	0.55 GB
SoundSpaces v1	soundspaces_v1/temporal	Temporal	700k	0.49 GB
SoundSpaces v1	soundspaces_v1/worldmem	WorldMem	700k	0.49 GB
SoundSpaces v2	soundspaces_v2/far_meta	FAR -- Meta	700k	0.55 GB
SoundSpaces v2	soundspaces_v2/far_multicue	FAR -- Multi-Cue	700k	0.55 GB
SoundSpaces v2	soundspaces_v2/temporal	Temporal	700k	0.49 GB
SoundSpaces v2	soundspaces_v2/worldmem	WorldMem	700k	0.49 GB

"Temporal" and "WorldMem" are the recency and field-of-view retrieval baselines, "LongLive-RAG" the content-query retrieval baseline; the FAR arms differ in the cues the retriever fuses (metadata, visual, multi-cue).

License

CC BY-NC 4.0. The generator and diffusion code these weights belong to are adapted from Navigation World Models and DiT (Meta Platforms, CC BY-NC 4.0); see the code repository's THIRD_PARTY_NOTICES.md.

Citation

@article{kim2026far,
  title   = {Learning What to Recall: Adaptive Multi-Cue Episodic Memory for World Models},
  author  = {Kim, Beomsu and Lai, Chieh-Hsin and Nguyen, Bac and Bar, Amir and Ye, Jong Chul and Mitsufuji, Yuki},
  journal = {arXiv preprint arXiv:2609.34677},
  year    = {2026},
  url     = {https://arxiv.org/abs/2609.34677}
}