ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models
ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models
This research introduces ALAM (Algebraically Consistent Latent Action Model), a method designed to overcome data scarcity in vision-language-action (VLA) models by extracting structural priors from action-free videos.
The paper introduces ALAM, a model that uses algebraically consistent latent transitions from action-free videos to improve vision-language-action (VLA) policy generation, reporting specific success rate improvements on MetaWorld and LIBERO benchmarks.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
Reported success rate
85%
Reported trials
View original evidence
ent transition geometry without requiring latent-to-action decoding. Representation probes show that ALAM reduces additivity and reversibility errors by 25-85 times over unstructured latent-action baselines and improves long-horizon cumulative reconstruction. When transferred to VLA policies, ALAM raises the average success rate from 47.9% to 85.0% on MetaWorld MT50 and from 94.2% to 98.1% on LIBERO, with consistent
Open source S5Reported success rate
98.1%
Reported trials
View original evidence
ent transition geometry without requiring latent-to-action decoding. Representation probes show that ALAM reduces additivity and reversibility errors by 25-85 times over unstructured latent-action baselines and improves long-horizon cumulative reconstruction. When transferred to VLA policies, ALAM raises the average success rate from 47.9% to 85.0% on MetaWorld MT50 and from 94.2% to 98.1% on LIBERO, with consistent
Open source S5Reported success rate
47.9%
Reported trials
View original evidence
ent transition geometry without requiring latent-to-action decoding. Representation probes show that ALAM reduces additivity and reversibility errors by 25-85 times over unstructured latent-action baselines and improves long-horizon cumulative reconstruction. When transferred to VLA policies, ALAM raises the average success rate from 47.9% to 85.0% on MetaWorld MT50 and from 94.2% to 98.1% on LIBERO, with consistent
Open source S5Reported success rate
94.2%
Reported trials
View original evidence
ent transition geometry without requiring latent-to-action decoding. Representation probes show that ALAM reduces additivity and reversibility errors by 25-85 times over unstructured latent-action baselines and improves long-horizon cumulative reconstruction. When transferred to VLA policies, ALAM raises the average success rate from 47.9% to 85.0% on MetaWorld MT50 and from 94.2% to 98.1% on LIBERO, with consistent
Open source S5reduction in additivity and reversibility errors
25–85
View original evidence
ent transition geometry without requiring latent-to-action decoding. Representation probes show that ALAM reduces additivity and reversibility errors by 25-85 times over unstructured latent-action baselines and improves long-horizon cumulative reconstruction. When transferred to VLA policies, ALAM raises the average success rate from 47.9% to 85.0% on MetaWorld MT50 and from 94.2% to 98.1% on LIBERO, with consistent
Open source S5reduction in additivity and reversibility errors upper bound
25–85
View original evidence
ent transition geometry without requiring latent-to-action decoding. Representation probes show that ALAM reduces additivity and reversibility errors by 25-85 times over unstructured latent-action baselines and improves long-horizon cumulative reconstruction. When transferred to VLA policies, ALAM raises the average success rate from 47.9% to 85.0% on MetaWorld MT50 and from 94.2% to 98.1% on LIBERO, with consistent
Open source S5
- code: Available Artifact link Open source S6
- paper: not_reported
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
ent transition geometry without requiring latent-to-action decoding. Representation probes show that ALAM reduces additivity and reversibility errors by 25-85 times over unstructured latent-action baselines and improves long-horizon cumulative reconstruction. When transferred to VLA policies, ALAM raises the average success rate from 47.9% to 85.0% on MetaWorld MT50 and from 94.2% to 98.1% on LIBERO, with consistent
Open source S5
gains on real-world manipulation tasks. Ablations further confirm that the strongest improvements arise from the synergy between algebraically structured latent transitions and joint flow matching. Our code is available at: https://github.com/Mark-zjtang/ALAM/tree/main.
Open source S6
Source：arXiv Robotics — research abstracts · arxiv.org