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README.md

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+---
+tags:
+  - futureworlds
+  - robotics
+  - world-model
+  - video-prediction
+  - reinforcement-learning
+---
+
+# FutureWorlds research checkpoints
+
+FutureWorlds learns action-conditioned robotic world models from alternative predicted futures. This repository contains the paired research checkpoints for RT-1, BridgeV2, and RoboCasa. The implementation, protocols, and reproduction instructions are in the [FutureWorlds GitHub repository](https://github.com/Alexander-wu/FutureWorlds).
+
+## Contents
+
+Each dataset has a `main` predictor, an SFT predictor, and `memspo200`, `grpo200`, and `ordinary200` comparison predictors. The dataset directory also contains its paired visual codec, action-range metadata, and configuration. RT-1 and RoboCasa predictors include trained text adapters. The original FP32 safetensors are kept without quantization or variant merging.
+
+| Dataset | Main checkpoint | Text conditioning |
+| --- | --- | --- |
+| RT-1 | MemSPO, 500 updates, R0 | T5-base plus trained adapters |
+| BridgeV2 | MemSPO, 200 updates, E4/R1 | None in this recipe |
+| RoboCasa | MemSPO, 400 updates, R0 | T5-base plus trained adapters |
+
+The external T5-base model is not copied into this repository. Use revision `a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1` of [`google-t5/t5-base`](https://huggingface.co/google-t5/t5-base) for the text-conditioned datasets.
+
+## Loading
+
+These are custom action-conditioned predictors, not generic `pipeline("text-to-video")` models. Use the dataset-specific codec, action ranges, and text adapter. The included `code/` directory contains the paired low-level checkpoint loader; see the [GitHub loading guide](https://github.com/Alexander-wu/FutureWorlds/blob/main/docs/LOADING.md) for the full RGB pipeline and the [model cards](https://github.com/Alexander-wu/FutureWorlds/tree/main/model_cards) for variant details. `main` and `memspo200` are different checkpoints.
+
+## Validation and limitations
+
+The exact uploaded-file hashes are recorded in `manifest.json`; `weights_manifest.json` records component provenance and marks whether each listed hash belongs to a source file or a release file. The code repository reports first-frame agreement with the original entry point on one fixed case per dataset. Full-horizon GPU reproduction of the consolidated release pipeline remains pending. Results in the paper and GitHub README are existing experiment results, not a fresh evaluation of this upload. The repository does not contain raw RT-1, BridgeV2, or RoboCasa datasets, optimizer states, or third-party baseline weights.
+
+## Rights and attribution
+
+No license has been selected for the original FutureWorlds additions or these checkpoints. Public visibility does not grant permission to use, modify, or redistribute them. Upstream components and the external T5 model retain their own licenses; see `licenses/` and [third-party notices](https://github.com/Alexander-wu/FutureWorlds/blob/main/THIRD_PARTY_NOTICES.md). Contact the project owner for permissions.

bridge/README.md

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+---
+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.
+
+## Checkpoint variants
+
+| 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.
+
+## Main-table evaluation
+
+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 |
+
+These are recorded paper results; staging weights does not constitute a fresh reproduction.
+
+## Required components
+
+- 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.
+
+## Loading and scope
+
+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.
+
+## Provenance and limitations
+
+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.

bridge/codec/config.json

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bridge/futureworlds_config.json

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+  "status": "staged_research_preview",
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+      "purpose": "matched_ablation",
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+    "ordinary200": {
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+      "method": "OrdinaryBeam",
+      "updates": 200,
+      "reward": "R0",
+      "purpose": "matched_ablation",
+      "text": false
+    }
+  }
+}

bridge/models/grpo200/config.json

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licenses/FSQ-LICENSE

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+MIT License
+
+Copyright (c) 2020 Phil Wang
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

licenses/RLVR-World-LICENSE

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+MIT License
+
+Copyright (c) 2025 THUML @ Tsinghua University
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

licenses/diffusers-LICENSE

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+                                 Apache License
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licenses/iVideoGPT-LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 THUML @ Tsinghua University
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
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+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

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rt1/README.md

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+---
+language:
+- en
+tags:
+- futureworlds
+- world-model
+- robotics
+- video-prediction
+- reinforcement-learning
+---
+# FutureWorlds — RT-1
+
+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.
+
+## Checkpoint variants
+
+| Folder | Method | Updates | Use |
+|---|---|---:|---|
+| `models/main` | MemSPO (R0) | 500 | 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.
+
+## Main-table evaluation
+
+128 trajectories, 32 predicted frames, 2 initial observations, per-frame Beam4, anchor1 + recent6, no true-future refresh. Recorded split: held-out development; runtime: GPU FP32.
+
+| PSNR ↑ | SSIM ↑ | LPIPS ↓ |
+|---:|---:|---:|
+| 22.4699 | 0.802595 | 0.155104 |
+
+These are recorded paper results; staging weights does not constitute a fresh reproduction.
+
+## Required components
+
+- Dataset-specific `codec/` and `preprocessing/` files, paired by the manifest.
+- Trained text adapters in each checkpoint and frozen T5-base at revision `a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1`; T5 is referenced, not duplicated.
+- 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.
+
+## Loading and scope
+
+Use `futureworlds.checkpoints.load_world_model(bundle_root, "rt1", 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.
+
+## Provenance and limitations
+
+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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