Vision-Language-Action or World-Action? Just Action Models
The paper introduces Just Action Models (JAM), robot control policies trained with an action loss and a foresight loss. The foresight loss trains current-observation features to predict frozen encodings of recorded future observations through a prediction head discarded after training, so no visual generation is used. JAM supports generator, vision-language, and self-supervised backbones. The authors report higher success rates than matched FastWAM and ImageWAM counterparts on LIBERO and LIBERO-Plus, similar or slightly higher rates on RoboTwin, and a 73% reduction in estimated online training FLOPs per sample per update relative to FastWAM with cached targets.
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The paper introduces Just Action Models (JAM), robot control policies trained with an action loss and a foresight loss. The foresight loss trains current-observation features to predict frozen encodings of recorded future observations through a prediction head discarded after training, so no visual generation is used. JAM supports generator, vision-language, and self-supervised backbones. The authors report higher success rates than matched FastWAM and ImageWAM counterparts on LIBERO and LIBERO-Plus, similar or slightly higher rates on RoboTwin, and a 73% reduction in estimated online training FLOPs per sample per update relative to FastWAM with cached targets.
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Key findings from the stored brief
- JAM predicts frozen encodings of recorded future observations, with the prediction head discarded after training.
- No visual generation is used during training or inference, and the backbone can be chosen freely.
- On LIBERO, JAM success rates improve from 97.0% to 98.2% relative to FastWAM, per the paper.
- With cached foresight targets, estimated online training FLOPs per sample per update fall by 73% relative to FastWAM.
Source claims not independently checked; FLOP figures are estimates reported by the authors, and results are on simulation benchmarks.
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Why this may matter
The work argues that future supervision can improve robot control without generative video machinery at inference, and that strong results are reachable with either generator-initialized or vision-language backbones, which could lower compute costs for policy training.
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