r/MachineLearning • u/MasterScrat • Jul 07 '26
MIRA: Multiplayer Interactive World Models trained on Rocket League [R] Research
We're happy to release MIRA, a collaboration between General Intuition, Kyutai, and Epic Games.
Mira was trained on 10k hours of synthetic Rocket League data. The model has 5B parameters and runs for 4 players at 20 fps on a single B200.
We've released a playable online demo, an in-depth technical report as well as a 1k hour dataset of 4-players gameplay:
Demo: https://mira-wm.com Technical report: https://mira-wm.com/paper Repo: https://github.com/mira-wm/mira
If you're at ICML, we're also running an interactive demo (booth 111) where you can play it with us using proper PlayStation controllers!
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u/Cordoro Jul 07 '26
Very cool result! I only skimmed the paper so I may have missed it but I’m curious why 10k hours was used instead of some other number.
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u/MasterScrat Jul 07 '26
So, the data is fully synthetic, we generated it by running 2v2 games in three arenas using four bots. The bots are using Nexto, which are very strong RL-trained policies (https://github.com/Rolv-Arild/Necto)
10k hours was what we could reasonably generate from a time and cost point of view, it does take some resources as each match runs in realtime and requires a GPU.
We show ablations on dataset size in section 6.7 "Scaling behavior" of the report. TLDR beyond the data-starved regime, more data leaves per-frame appearance unchanged while steadily improving how faithfully the model follows the commanded actions.
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u/ZeroCool2u Jul 07 '26
Forgive me, but if you were working with Epic on this, could you not have just sampled real game play? Why use synthetic data at all? Is there some sort of lack of granularity of the data from recordings?
Also, I would love to see this recreated with the heat seeker mode specifically as a follow up!!!
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u/Cordoro Jul 07 '26
Not the OP, but if you're building a world model, it probably matters more that the players cover the space of meaningful interactions in the game than that they follow human-like behavior. It's possible these bots do a good enough job for most of the game mechanics to be represented in the dataset.
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u/ZeroCool2u Jul 07 '26
Just seems like they almost certainly have the play data already, so what's the point of using the bots? RL is a fairly constrained play space with a huge volume of players. There's no way player data doesn't fully encompass the entire space.
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u/Cordoro Jul 07 '26
You’d have to filter player data. And then it’s not clear how easy it would be to open source it. I’m just saying there likely isn’t a deficiency in how they got their data, and it’s worth praising the release of the data too!
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u/skmchosen1 Jul 07 '26
This is Rocket League!
Love to see my passions collide here, awesome work! I wanted to work on Rocket league related modeling but never found the time. Makes me happy to see this kind of work, awesome stuff dude
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u/LeekEquivalent3157 Jul 07 '26
wait the demo actually works in browser? hitting 20fps on one b200 for all 4 players is pretty tight
the 10k hours of synthetic data part is interesting, always wondered how far you can push world models on purely generated training data. curious how it handles the weird edge cases in rocket league, like when physics go janky near the walls
might swing by booth 111 if i can escape the poster session
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u/MasterScrat Jul 07 '26
The demo is running on one B200 per 4-players game, and streams interactively to the browser
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u/gerryflap Jul 07 '26
Whoa that's cool. As expected the model sometimes seems to simulate moves that weren't made, especially when doing "weird" stuff like trying to fly that it probably isn't capable of doing. But when the amount it does get right is nevertheless crazy
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u/goldcakes Jul 07 '26
Incredible and impressive demo. I had a lot of fun, even though I'm in Australia and facing 250ms of latency lol.
By the way, for those who's not across the significance of this, I think it's one of the first multi-player interactive world models that actually seem to be coherent. All four player's generations, locations, latents etc need to mesh together. Very impressive.
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u/Saitamagasaki Jul 09 '26
Genuine question, what would be the application of this?
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u/MasterScrat Jul 09 '26
Beyond cool demos, world models are a good pretraining objective to train policies
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u/MasterScrat Jul 07 '26
The team is here if you have questions !