| --- |
| language: |
| - en |
| tags: |
| - futureworlds |
| - world-model |
| - robotics |
| - video-prediction |
| - reinforcement-learning |
| --- |
| # FutureWorlds — BridgeV2 |
|
|
| Local release candidate, not yet published. Model weights are verified by CPU SHA-256; end-to-end inference with the portable RGB pipeline is not yet validated. |
|
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| ## Checkpoint variants |
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|
| | Folder | Method | Updates | Use | |
| |---|---|---:|---| |
| | `models/main` | MemSPO (R1 / E4) | 200 | Paper main table | |
| | `models/sft` | SFT | 50,000 | Initialization/control | |
| | `models/memspo200` | MemSPO (R0) | 200 | Matched ablation | |
| | `models/grpo200` | GRPO (R0) | 200 | Matched ablation | |
| | `models/ordinary200` | Ordinary global beam8, top4 (R0) | 200 | Proposal-strategy ablation | |
|
|
| All post-training checkpoints start from their paired SFT checkpoint. `main` and `memspo200` must not be interchanged even though both use 200 updates: Bridge main uses R1, while the matched ablation uses R0. |
|
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| ## Main-table evaluation |
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| 128 trajectories, 32 predicted frames, 2 initial observations, per-frame Beam4, anchor1 + recent6, no true-future refresh. Recorded split: held-out development; runtime: CPU FP32. |
|
|
| | PSNR ↑ | SSIM ↑ | LPIPS ↓ | |
| |---:|---:|---:| |
| | 22.0276 | 0.796801 | 0.140858 | |
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|
| These are recorded paper results; staging weights does not constitute a fresh reproduction. |
|
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| ## Required components |
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| - Dataset-specific `codec/` and `preprocessing/` files, paired by the manifest. |
| - This released Bridge branch does not use text conditioning. |
| - 13 action tokens per predicted frame. Use the dataset adapter and recorded action ranges, not another dataset’s statistics. |
| - Original FP32 safetensors; no post-hoc quantization or checkpoint merging. |
|
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| ## Loading and scope |
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| Use `futureworlds.checkpoints.load_world_model(bundle_root, "bridge", variant="main")` for the predictor only. This does not decode RGB, prepare actions, or download T5 automatically. A complete RGB demo and clean-environment GPU equivalence run remain pending. These models are custom action-conditioned predictors, not generic `pipeline("text-to-video")` models. |
|
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| ## Provenance and limitations |
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| Core architecture and tokenizer derive from RLVR-World/iVideoGPT. Upstream license texts are included under `licenses/`; the license for original additions is not yet selected. The raw datasets and third-party baseline weights are not included. Learned-world feedback examples are qualitative generated trajectories, not independently verified physical task-success results. |
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