Model Card for MIRA Mini 4P
The four-player multiplayer tier of MIRA Mini: a 1B action-conditioned world model
that simulates a 2v2 Rocket League match for four players at once, each with their
own first-person view, conditioned jointly on all four action streams. It is our
reproduction of the MIRA multiplayer recipe at 1B scale (the original demo is 5B).
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
The multiplayer wrapper tiles the four players' views into one joint latent frame
(stacked along height) and runs a single diffusion transformer over the tile, so all
four views are predicted jointly and stay mutually consistent: one ball, one match,
four synchronized dreams of it. Per-player action streams are embedded, combined, and
condition every step. Training uses the paper's per-player action dropout, so any seat
can be handed to the model instead of a controller: the model imagines what that player
would have done (the autopilot mode in the demo).
Following the paper's budget-split finding, the model is warm-started from our
single-player checkpoint (52k steps) and fine-tuned on four-view multiplayer data.
Two inference-time options ship with the serving stack, both documented in the
technical report: an action-guidance mode that amplifies a player's steering authority
(useful at this model's training budget, where early-window controllability is still
maturing), and a distilled small decoder that renders the same latents about 8x cheaper.
- Developed by: Alakazam
- Model type: Action-conditioned multiplayer 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-4p
That starts the model, a room relay, and a web UI; share the room code and up to four
people play in the same simulated match (one GPU per room). Seats without a human are
driven by the model (autopilot). The bundle is world_model_config.yaml,
checkpoint-<step>/checkpoint.pth, codec/, context/default.npz.
Training Details
Training Data
kyutai/rocket-science: four
synchronized first-person recordings per match with per-player action streams, released
by Kyutai under CC BY-NC-SA 4.0 with Epic Games' permission. About 15,800 matches,
roughly 2,000 hours. We trained on the released dataset with no additions.
Training Procedure
The paper's two-stage recipe: single-player pretraining (52k steps), then the
multiplayer fine-tune on 4 nodes x 8 H100 (preemptible), global batch 32 tiled frames
(128 player-views per step), per-player action dropout p=0.1, checkpoint every 1,000
steps with a fleet-consistent resume protocol that survived four spot preemptions with
zero lost steps. Evaluations were pre-registered at a 63k freeze; the released checkpoint
is 90k multiplayer steps, requalified on the full suite before shipping (the paper's demo
model trains 100k on ~5x the data at 5B parameters; treat this tier accordingly).
Evaluation
| Metric | MIRA Mini 4P | MIRA (paper) | Note |
|---|
| MP val loss | monotone through the 90k release (0.320 at 80k, last validation) | n/a | no overfit signature |
| MP gFID (4s, 10-step paper protocol) | 24.1 at release; 24.7 at the 63k freeze (27.3 at 50k, improving) | 9.4 to 9.9 | paper is 5B / 100k steps / ~5x data; paired-Frechet, floor ~6; at the 8-step serving setting (freeze): 28.7/29.0 |
| Long-rollout stability | 10 min, zero freezes; 30s park-OOD melts 4.1x at the freeze, snaps back in 10s; at release the same park produces no visual melt (drift <=1.6x baseline) and the largest excursions (2.7x) are hallucinated goal/replay broadcast events, frames coherent | "stable indefinitely" | first 10-minute rollouts on this tier; evidence clips in the repo |
| Controllability | measured per-checkpoint | ARR (Fig. 13 of the paper) | our seed-controlled divergence ladder; full curves in the report |
Controllability at this budget is the honest caveat: steering is measurable and improves
with training (the report publishes the per-checkpoint curve; early-window authority
0.281 un-guided at the 90k release vs 0.261 at the freeze, and 0.355 at the demo's
default guidance w=4), and the serving stack's
action-guidance option amplifies it at inference; boost and jump respond less than
steering, matching the paper's finding that rare actions are recovered last.
Performance
| Runtime | fps (B200, 8 steps, four views) |
|---|
| Reference implementation | ~8.6 |
| FlashDreams CUDA-graph port (bit-exact) | ~10.8 |
| + distilled small decoder | higher; measured numbers in the technical report |
Bias, Risks, and Limitations
- Bot-collected Rocket League only; no transfer beyond the game.
- Early-window steering authority is below the single-player model's at this training
budget (quantified in the report); action guidance narrows the gap at ~2x inference cost
and is applied per view (the commanded car's view only — a scalar guidance weight
amplifies the driver's actions onto every view).
- Action attribution is a trained property, incomplete at this budget: all four players'
actions are mean-pooled into one conditioning stream, so commanded actions bleed weakly
into other views as a global motion prior (measured in the report; sharpens with
training).
- Replays after goals are hallucinated (the context window is ~4 s, matching the paper's
own limitation), and out-of-distribution play can degrade the image before it recovers.
- 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)
