ALAM: Algebraically Consistent Latent Action Model for Vision-Language-Action Models
Overview
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.
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Event evidence and corrections
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Reported quantity · Reported success rate: 85 percent · reported trials · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 98.1 percent · reported trials · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 47.9 percent · reported trials · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 94.2 percent · reported trials · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Reported quantity · reduction in additivity and reversibility errors: 25 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Reported quantity · reduction in additivity and reversibility errors upper bound: 25 other · Basis not reported
“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”
Exact source · revision 1Source owner not reported
Artifact availability · code: available
“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.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalALAM: 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.
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