Model Card for Vintix
This is a multi-task action model via in-context reinforcement learning
Model Details
Setting | Description |
---|---|
Parameters | 332M |
Model Sizes | Layers: 20, Heads: 16, Embedding Size: 1024 |
Sequence Length | 8192 |
Training Data | MuJoCo, Meta-World, Bi-DexHands, Industrial Benchmark |
Model Description
- Developed by: dunnolab
- License: Apache 2.0
Model Sources
- Repository: https://github.com/dunnolab/vintix
- Paper: https://arxiv.org/abs/2501.19400
Citation
@article{polubarov2025vintix,
author={Andrey Polubarov and Nikita Lyubaykin and Alexander Derevyagin and Ilya Zisman and Denis Tarasov and Alexander Nikulin and Vladislav Kurenkov},
title={Vintix: Action Model via In-Context Reinforcement Learning},
journal={arXiv},
volume={2501.19400},
year={2025}
}
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Evaluation results
- Normalized Score IQM (95% CI) on MuJoCoself-reported0.990
- Normalized Score IQM (95% CI) on Meta-Worldself-reported0.990
- Normalized Score IQM (95% CI) on Bi-DexHandsself-reported0.920
- Normalized Score IQM (95% CI) on Industrial-Benchmarkself-reported0.990
- Total reward on ant_v4self-reported6315.00 +/- 675.00
- Expert normalized total reward on ant_v4self-reported0.98 +/- 0.10
- Total reward on halfcheetah_v4self-reported7226.50 +/- 241.50
- Expert normalized total reward on halfcheetah_v4self-reported0.93 +/- 0.03
- Total reward on hopper_v4self-reported2794.60 +/- 612.62
- Expert normalized total reward on hopper_v4self-reported0.86 +/- 0.19