Model Card for MIRA Mini PSD
The few-step variant of MIRA Mini: the same 1B action-conditioned world model of
Rocket League, distilled so that two sampling steps replace eight. The sampler does
4x less work per frame; delivered frame rate on the same GPU is about 1.3x (14.5 to
19.4 fps measured on L40S) because decode still dominates, until the small decoder
is swapped in. Visual quality matched the 8-step baseline on our evaluations.
Built on
MIRA, released July 6, 2026 by
General Intuition and
Kyutai
with Epic Games:
code,
dataset, and a published training
recipe. MIRA Mini is Alakazam's independent reproduction and optimization of that work.
Model Details
Model Description
Identical architecture to
alakazamworld/mira-mini
(a 1B diffusion transformer in the latent space of a representation-autoencoder codec),
plus the paper's progressive self-distillation (PSD), applied post hoc. Sampling a latent
frame integrates the flow-matching field; PSD distills the model so one large integration
step replaces two smaller ones, which lets two steps reach the quality that previously
took eight.
The paper distills its pretrained model and leaves unspecified how the step-size (Δ)
conditioning pathway is introduced into a checkpoint trained without one. This model uses
our construction: the pathway is injected with a zero-initialized output projection, so at
step 0 the model is identical to the base checkpoint, and the distillation signal fades in
from there (10k steps, learning rate 3e-5, warmup 100; the from-scratch learning rate
diverges on a converged checkpoint).
- Developed by: Alakazam
- Model type: Action-conditioned world model (interactive video generation)
- License: CC BY-NC-SA 4.0, inherited from the training dataset
- Reproduction of: MIRA (General Intuition and Kyutai, with Epic Games)
This model is for demonstration and research only. The training dataset
(
kyutai/rocket-science) is
CC BY-NC-SA 4.0, with Rocket League content used by Epic Games' permission. These weights
inherit that license: non-commercial, share-alike, with attribution.
These weights are an independent release by Alakazam. They are not released by, associated with, or endorsed by General Intuition, Kyutai, or Epic Games.
Model Sources
How to Get Started
1pip install alakazam-mira-mini
2mira-mini play --model mira-mini-psd
The bundle is world_model_config.yaml, checkpoint-10000/checkpoint.pth, codec/,
context/default.npz. Two steps is the intended setting; 4 and 8 also work (quality is
flat across them after distillation, so extra steps mostly cost frame rate).
Training Details
Post-hoc PSD fine-tune of the mira-mini 1B checkpoint: 10k steps on 8 preemptible H100s,
PSD loss applied stochastically on 10% of updates (the paper's mixing rate), ground-truth
flow term otherwise. Validation loss improved from 0.3952 (base, at handoff) to 0.3741,
and the 2-step teacher-forced LPIPS ends below the base model's 2-step LPIPS with the
margin still growing at 10k (−0.0021 on our held-out shard).
Training data (through the base model):
kyutai/rocket-science,
as released, no additions.
Performance
| Hardware | 8 steps (base) | 2 steps (this model) |
|---|
| L40S | 14.5 fps | 19.4 fps measured (live serving probe) |
| B200 | 25.7 fps | not benched at 2 steps |
| L4 | 5.4 fps | not benched at 2 steps |
Measured numbers come from serving probes on the stated hardware; estimates are marked.
The serving runtime is the FlashDreams CUDA-graph port (bit-exact against the reference
implementation at matched settings).
Bias, Risks, and Limitations
- Same limitations as the base model: bot-collected Rocket League only, no transfer,
plausible continuations rather than exact physics.
- Few-step sampling drifts slightly more per step than 8-step sampling on long rollouts;
quantified in the report's physics-verification section: in ten-minute rollouts the
distilled rungs ride an elevated-but-flat distance band (a level shift, not progressive
melting), and per-action recoverability degrades tail-first (rare actions like air-rolls
before common ones) — aggregate numbers hide exactly that.
- Non-commercial license, inherited from the dataset.
Citation
Cite the MIRA paper and link this repository.
1@article{hu2026mira,
2 title = {Multiplayer Interactive World Models with Representation Autoencoders},
3 author = {Hu, Anthony and others},
4 year = {2026},
5 note = {arXiv:2607.05352}
6}
Model Card Authors
Alakazam (alakazam.gg)
