THETA Bench
Collection
THETA Bench simulation policies (3,003 segments), selected real G1 adaptations (91 demos), and training data. • 15 items • Updated
THETA Bench Cosmos3 Edge Policy: Simulation training, 3,003 segments
The validated final policy checkpoint is available in this repository.
This is a THETA training-result repository. It does not substitute an upstream pretrained policy for a THETA-trained checkpoint.
| Setting | Value |
|---|---|
| Training stage | Simulation training, 3,003 segments |
| Target optimizer updates | 40000 |
| Per-GPU batch / GPUs / global batch | 16 / 8 / 128 |
| Gradient accumulation | 1 |
| Conditions per global batch | 18 |
| Dataset revision | 8b2cd31e107b64cb13f812ea217a63a20845c78a |
The simulation pool contains 1,200 successful L1/L2 demonstrations and 1,803 extracted L0 prefixes, spanning 18 conditions. The 3,003 segments are not 3,003 independent demonstrations.
Use the model's native THETA adapter and model-specific dependencies. This repository does not claim compatibility with arbitrary Transformers or simulation loaders. No evaluation score is claimed by checkpoint publication.
Native model-only Cosmos3 DCP; optimizer and trainer state excluded. Requires the pinned Cosmos framework and THETA adapter. Run prepare_inference.py with the downloaded repository root, then use the resulting absolute inference-local.json with --checkpoint /model --dataset /dataset --config /inference-local.json. The trained config, raw action units, VAE, and native processor are retained. Native final checks and a fresh CPU model scan passed. The native validator has no historical payload hashes; export hashes identify the bytes checked at publication. GPU reload/evaluation is a separate operation.
Training uses independent model optimizers and shared GPU execution through MPS. Publication is performed by a CPU uploader after final checkpoint validation.