minejepa-treechop-4.6m

MineJEPA Treechop 4.6M

MineJEPA Treechop 4.6M is an action-conditioned latent world model trained on MineRL Treechop human demonstrations. It predicts future latent states from a current RGB observation and a sequence of MineRL action vectors. It is not a pixel/video generator and is not a Minecraft-playing agent.

Model summary

• Architecture: convolutional RGB encoder + residual action-conditioned MLP dynamics + deterministic EMA target encoder.
• Parameters: approximately 4.6M trainable parameters.
• Input: 64x64 RGB MineRL POV frames and 10-dimensional flattened MineRL action vectors.
• Training: 200k updates, batch size 32, horizons 1/3/5/10, with an action-contrastive objective at horizons 3 and above.
• Recommended artifact: checkpoints/checkpoint-200k.safetensors.

Training data and split

The model was trained on 209 MineRLTreechop-v0 human demonstration trajectories. The converted local Lance dataset contained 446,574 16-frame windows. An 80/20 whole-episode split, seed 17, reserved 42 trajectories from training for evaluation.

This repository does not redistribute MineRL data, converted Lance shards, raw frames, or action trajectories. Users must download source data through MineRL and comply with its terms before reproducing conversion or training.

The matching MineJEPA Treechop conversion manifest records the exact conversion schema, action encoding, split, and local reproduction command used for this release.

Held-out results

The following results compare the recorded action sequence with mismatched action sequences while holding the start observation and true future target fixed. Metrics aggregate three deterministic samples of 512 windows from the same 42 held-out episodes.

action margin is recorded-target cosine minus mismatched-action-target cosine. It measures whether the model's predicted future is more compatible with the action sequence that actually occurred.

Intended use

Use this checkpoint for research into latent dynamics, counterfactual action diagnostics, feature retrieval, and offline planning experiments on compatible MineRL Treechop observations and action encoding.

Limitations

• The model has no pixel decoder and cannot generate Minecraft video.
• It was evaluated on one task and one episode-level split only.
• It is not validated for real-time environment control.
• Its H=10 goal-action retrieval is only 3.1% R@1 among 256 candidate action chunks. It is therefore not a reliable large-library goal planner.
• Do not use it outside research/educational contexts without independent validation.

Loading

Install the repository at the release commit, download config.json and checkpoint-200k.safetensors, then load the full state dict into MineJEPAWorldModel(action_dim=10, latent_dim=512, hidden_dim=1024) using safetensors.torch.load_file.

For exact commands and fine-tuning guidance, see the GitHub repository's docs/MINEJEPA_HF_RELEASE.md.

Citation

If you use this release, cite the MineJEPA-SWM repository and link to its evaluation reports. This is an independent research implementation and is not an official MineRL or Stable World Model checkpoint release.