Generalizable VLA Finetuning via Representation Anchoring and Language-Action Alignment

Hugging Face Daily Papers Papers
vla fine-tuning representation-anchoring language-action-alignment robotics behavior-cloning generalization

Summary

Anchor-Align augments behavioral cloning with vision-language anchoring to preserve pretrained representations and language-action alignment, improving real-robot success rates by over 20% on xArm7 and showing consistent gains in simulation benchmarks.

Finetuning a pretrained vision-language model (VLM) on robot demonstrations via behavior cloning (BC) has become the standard recipe for vision-language-action (VLA) policies. However, BC finetuning progressively overwrites the pretrained representations that support visual and semantic generalization. Co-training on web image-text data, a common remedy, does not prevent this; it applies language and action losses to separate observations, leaving VLAs with language-action misalignment that standard manipulation benchmarks do not expose. We propose Anchor-Align, which augments BC with two objectives: Vision-Language Anchoring distills layer-wise representations from a frozen VLM copy to prevent this drift, while Language-Action Alignment converts each action target into a discrete motion-direction label and jointly trains language and action prediction on the same robot observation. On a physical xArm7 robot, across two widely used VLA architectures, Anchor-Align improves real-robot success on both (28% to 54% and 37% to 60%). At scale in simulation, we demonstrate consistent improvements on OOD perturbations, perceptual robustness, and long-horizon control across LIBERO-PRO, LIBERO-Plus, and CALVIN, respectively, suggesting that preserving pretrained representations and effective action learning are not fundamentally at odds. Project page: anchoralignvla.github.io
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Paper page - Generalizable VLA Finetuning via Representation Anchoring and Language-Action Alignment

Source: https://huggingface.co/papers/2607.13429

Abstract

Finetuningapretrainedvision-languagemodel(VLM)onrobotdemonstrationsviabehaviorcloning(BC)hasbecomethestandardrecipeforvision-language-action(VLA)policies.However,BCfinetuningprogressivelyoverwritesthepretrainedrepresentationsthatsupportvisualandsemanticgeneralization.Co-trainingonwebimage-textdata,acommonremedy,doesnotpreventthis;itapplieslanguageandactionlossestoseparateobservations,leavingVLAswithlanguage-actionmisalignmentthatstandardmanipulationbenchmarksdonotexpose.WeproposeAnchor-Align,whichaugmentsBCwithtwoobjectives:Vision-LanguageAnchoringdistillslayer-wiserepresentationsfromafrozenVLMcopytopreventthisdrift,whileLanguage-ActionAlignmentconvertseachactiontargetintoadiscretemotion-directionlabelandjointlytrainslanguageandactionpredictiononthesamerobotobservation.OnaphysicalxArm7robot,acrosstwowidelyusedVLAarchitectures,Anchor-Alignimprovesreal-robotsuccessonboth(28%to54%and37%to60%).Atscaleinsimulation,wedemonstrateconsistentimprovementsonOODperturbations,perceptualrobustness,andlong-horizoncontrolacrossLIBERO-PRO,LIBERO-Plus,andCALVIN,respectively,suggestingthatpreservingpretrainedrepresentationsandeffectiveactionlearningarenotfundamentallyatodds.Projectpage:anchoralignvla.github.io

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