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README.md

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+# RLWF — DreamZero checkpoints
+
+Private checkpoint repository for the RLWF paper ("Active Robot Data Collection
+from World Model Feedback"). Two checkpoints, both **stock DreamZero
+architecture, no architectural modifications** — only the training data and
+training-config differ.
+
+## Layout
+
+```
+rlwf-ckpt/
+├── README.md
+├── LICENSE
+├── mimicgen-core-14b-lora-step80000/   # LoRA fine-tune, ~217 MB
+└── mimicgen-core-14b-full-step46000/   # full fine-tune, 10-shard ~47 GB
+```
+
+## What each checkpoint is
+
+### `mimicgen-core-14b-lora-step80000/`
+
+- **Architecture**: stock DreamZero (`groot.vla.model.dreamzero.base_vla.VLA`)
+- **Base model**: Wan2.1-I2V-14B-480P, frozen
+- **Adapter**: LoRA, rank 4, target modules `q,k,v,o,ffn.0,ffn.2`
+- **Action head**: WAN flow-matching action transformer
+  (`groot.vla.model.dreamzero.action_head.wan_flow_matching_action_tf.WANPolicyHead`)
+- **Action dim**: 32 (multi-embodiment), horizon 24
+- **Training data**: MimicGen expert demos on LIBERO MimicGen-core (12 tasks)
+- **Step**: 80,000
+
+### `mimicgen-core-14b-full-step46000/`
+
+- **Architecture**: same stock DreamZero, no changes
+- **Variant**: full fine-tune (no LoRA) on 16 GPUs with DeepSpeed ZeRO
+- **Sharding**: 10-shard safetensors (`model-{1..10}-of-00010.safetensors`)
+- **Training data**: same MimicGen-core 12 tasks, longer instruction prompts
+  ("detailed_instruct" recipe)
+- **Step**: 46,000
+
+## How to load
+
+With the DreamZero codebase available:
+
+```python
+from stable_worldmodel.wm.utils import load_pretrained
+# either subdir works the same way:
+model = load_pretrained(
+    "MinghaoFu/rlwf-ckpt/mimicgen-core-14b-lora-step80000",
+    extra_args={"torch_dtype": "bfloat16"},
+)
+```
+
+Direct safetensors load (LoRA, single file):
+
+```python
+from safetensors.torch import load_file
+state_dict = load_file("model.safetensors")
+```
+
+Direct safetensors load (full, sharded):
+
+```python
+import json
+from safetensors.torch import load_file
+
+with open("model.safetensors.index.json") as f:
+    index = json.load(f)
+state_dict = {}
+for shard in sorted(set(index["weight_map"].values())):
+    state_dict.update(load_file(shard))
+```
+
+Full training config is in `experiment_cfg/conf.yaml` of each subdir.
+
+## What is NOT in this repo
+
+- DeepSpeed optimizer state (`global_step*/`) — stripped to keep the download
+  small. If you want to resume training instead of just loading for inference,
+  ping me; the optimizer shards are kept separately.
+- `rng_state_*.pth` — same reason.
+- The `latest` text file — points to a path inside `global_step*/`, irrelevant
+  without the optimizer state.
+
+## License
+
+MIT (see `LICENSE`). The underlying Wan2.1-I2V-14B-480P base model has its own
+Apache-2.0 license. DreamZero architecture follows the original
+authors' release terms; this repo only redistributes the fine-tuned weights.
+
+## Contact
+
+Minghao Fu — isminghaofu@gmail.com