CogWAM — RoboDojo, 25-step horizon, 50k steps

Released weights for the CogWAM recipe cogwam_robodojo_causal_dino_mot_h25_eventmem_dino_multilayer_50k_v1, taken at optimizer step 50,000.

Contents

File	Size
model.safetensors	7.80 GiB	2272 BF16 tensors: the framework state dict
checkpoint_keys.json	215 KiB	key -> [dtype, shape], for validating without loading weights
dataset_statistics.json	5 KiB	action/state normalisation — the policy server cannot un-normalise actions without it
inference_config.yaml		the resolved configuration this checkpoint was trained with
artifact_manifest.json		sha256 of each file, model geometry, backbone requirements

What is NOT here

The two backbones are referenced, not bundled, because one of them may not be redistributed:

Backbone	Where to get it	Licence
RynnBrain1.1-2B	Alibaba-DAMO-Academy on Hugging Face	Apache-2.0
DINOv3 ViT-B/16	facebook/dinov3-vitb16-pretrain-lvd1689m	Meta dinov3-license, gated — accept Meta's terms yourself

The DINO teacher is also genuinely absent from the state dict: it is attached outside the module tree and its normalisation buffers are non-persistent, so it never enters state_dict().

Loading

export COGWAM_BASE_VLM=/models/rynnbrain1.1-2B
export COGWAM_DINO_MODEL=/models/dinov3-vitb16

python tools/verify_checkpoint.py --artifact <this directory> --build

--build constructs the framework from inference_config.yaml and loads with strict=True. No key remapping is needed or performed: checkpoint key prefixes come from nn.Module attribute names, not from the framework class name.

To serve it:

COGWAM_ARTIFACT_DIR=<this directory> NUM_SERVERS=8 bash scripts/serve_policy.sh

Geometry

Tensors / dtype	2272 / BF16
Prefixes	action_model. 1652, qwen_vl_interface. 618, world_plan_queries.embedding, action_plan_queries.embedding
Action horizon / dim	25 / 14
World queries	16
Planner hidden	2048
Vocabulary	248081 (includes <WORLD_PLAN>, <ACTION_PLAN>, <KEEP>, <UPDATE>)
MoT	30 layers; world 512, action 1024
DINO embedding	768

Two digests are recorded in the manifest and they answer different questions: model.key_inventory_sha256 covers names/shapes/dtypes, so two runs of the same recipe share it; only files["model.safetensors"].sha256 identifies these particular weights.

Evaluation

Run against RoboDojo 0.2.0 at commit 9b4cc885e8f530ed3ab14a30a312ae70242771c4. Protocol, chunk/replan semantics and the event-memory state machine are documented in the repository under docs/evaluation.md. Published results are not included with this release.

Licence

MIT for the code; these weights are released under the same terms. The two backbone models carry their own licences, listed above.