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CrafterDojo v1.0 weights (safetensors)

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README.md ADDED
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+ ---
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+ license: mit
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+ tags:
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+ - reinforcement-learning
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+ - embodied-ai
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+ - behavior-cloning
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+ - crafter
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+ - craftax
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+ - video-language
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+ ---
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+
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+ # CrafterDojo v1 weights
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+
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+ Pretrained models from **CrafterDojo: An Integrated Research Suite for Building Open-Ended Embodied Agents in Crafter**
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+ ([paper](https://arxiv.org/abs/2508.13530), [code](https://github.com/frechele/CrafterDojo), [project](https://sites.google.com/view/crafterdojo)).
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+
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+ | File | Model | Notes |
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+ |---|---|---|
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+ | `vpt/tiny.safetensors` | CrafterVPT-tiny | behavior cloning, last checkpoint |
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+ | `vpt/base.safetensors` | CrafterVPT-base | behavior cloning, last checkpoint |
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+ | `vpt/large.safetensors` | CrafterVPT-large | behavior cloning, last checkpoint |
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+ | `crafterclip/crafterclip.safetensors` | CrafterCLIP | video–caption encoder (MineCLIP architecture, ViT-B/16 backbone) |
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+ | `steve1/steve1.safetensors` | CrafterSteve-1 | instruction-following policy (LoRA on CrafterVPT-base) |
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+ | `steve1/steve1_prior.safetensors` | CrafterSteve-1 prior | text → visual-goal CVAE prior |
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+ | `vpt/*.yaml` | architecture configs | same files as `models/vpt/*.yaml` in the code repository |
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+
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+ All models are trained on the `sv_gh_pp-10B_20ep` CrafterPlay mixture (Survival + Gatherer experts) in Craftax-Classic.
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+ Each `.safetensors` file records its training run in the header metadata.
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+
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+ ## Usage
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+
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+ ```bash
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+ git clone https://github.com/frechele/CrafterDojo && cd CrafterDojo
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+ bash scripts/download_weights.sh # downloads this repository (revision v1.0) into models/
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+ ```
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+
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+ Load with the repository's helpers (`crafterdojo.common.checkpoint.load_state_dict`), which also restore the
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+ shared CLIP tensors in CrafterCLIP (stored once, listed in the `aliases` metadata entry).
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @article{park2025crafterdojo,
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+ title = {CrafterDojo: An Integrated Research Suite for Building Open-Ended Embodied Agents in Crafter},
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+ author = {Park, Junyeong and Cho, Hyeonseo and Ahn, Sungjin},
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+ journal = {arXiv preprint arXiv:2508.13530},
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+ year = {2025}
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+ }
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+ ```
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+ model:
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+ args:
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+ net:
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+ args:
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+ active_reward_monitors:
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+ env_reward: 1
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+ attention_heads: 4
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+ attention_mask_style: clipped_causal
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+ attention_memory_size: 128
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+ diff_mlp_embedding: false
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+ hidsize: 512
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+ img_shape:
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+ - 144
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+ - 144
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+ - 3
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+ impala_chans:
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+ - 16
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+ impala_kwargs:
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+ post_pool_groups: 1
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+ init_norm_kwargs:
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+ batch_norm: false
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+ group_norm_groups: 1
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+ n_recurrence_layers: 4
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+ only_img_input: true
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+ pointwise_ratio: 4
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+ pointwise_use_activation: false
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+ recurrence_is_residual: true
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+ recurrence_type: transformer
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+ timesteps: 64
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+ use_pointwise_layer: true
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+ use_pre_lstm_ln: false
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+ pi_head_opts:
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+ temperature: 1.0
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+ attention_heads: 4
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+ attention_memory_size: 128
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+ diff_mlp_embedding: false
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+ lora:
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+ alpha: 16
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+ r: 16
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