Title: Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better
Venue: NeurIPS 2025
Accepted: yes
Keywords: vision-language-action models

robotic manipulation

robot learning
Abstract: Vision-language-action (VLA) models provide a powerful approach to training control policies for physical systems, such as robots, by combining end-to-end learning with transfer of semantic knowledge from web-scale vision-language model (VLM) training. However, the constraints of real-time control are often at odds with the design of VLMs: the most powerful VLMs have tens or hundreds of billions of parameters, presenting an obstacle to real-time inference, and operate on discrete tokens rather than the continuous-valued outputs that are required for controlling robots. To address this challenge, recent VLA models have used specialized modules for efficient continuous control, such as action experts or continuous output heads, which typically require adding new untrained parameters to the pretrained VLM backbone. While these modules improve real-time and control capabilities, it remains an open question whether they preserve or degrade the semantic knowledge contained in the pretrained VLM, and what effect they have on the VLA training dynamics. In this paper, we study this question in the context of VLAs that include a continuous diffusion or flow matching action expert, showing that naively including such experts significantly harms both training speed and knowledge transfer. We provide an extensive analysis of various design choices, their impact on performance and knowledge transfer, and propose a technique for insulating the VLM backbone during VLA training that mitigates this issue. Videos are available at https://pi.website/research/knowledge_insulation and open-source model weights are available at https://github.com/Physical-Intelligence/openpi.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper primarily investigates how to better train VLA models that incorporate diffusion or flow matching action experts. The proposed approach involves training the VLM backbone with discrete actions, training the action expert with continuous actions, and stopping gradient flow from the action expert to the VLM backbone. The authors conduct extensive real-world experiments to validate both the performance of their model and the effectiveness of the proposed training scheme.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n\n1. The paper clearly states its motivation and is well-written and easy to follow.\n\n2. It proposes an effective training method for VLA models that use diffusion or flow matching action experts.\n\n3. The experiments are thorough and well-designed, providing strong empirical evidence to support the paper’s claims and demonstrating the advantages of the proposed model.\n\nWeaknesses\n\n1. It appears that part of the robot action data used to train the model is not open-sourced, which may hinder reproducibility.\n\nQUESTIONS:\nQuestions\n\n1. If the VLM backbone is first pretrained using discrete action data (with VLM data), and then frozen while only finetuning the action expert, would this yield similar results to jointly training the VLM backbone and action expert with gradient stopping?\n\n2. In Figure 7, why do Pi0 and joint training without VLM data perform better in the OOD setting than in the ID setting? Additionally, the performance gap between ID and OOD for the proposed method is relatively small. Does this suggest that the OOD tasks are too simple or not sufficiently distinct from the ID tasks?\n\n3. Section A.3 of the supplementary material lists several VLM tasks used during training. To what extent does the object localization task contribute to the model’s overall performance?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method for training generalist VLA models by co-training on large robot and VLM datasets. To address the challenge that naive co-training may hinder effective knowledge transfer from the VLM to robotics, this paper proposes a stop-gradient strategy. Specifically, it stops the gradient flow from the action expert to the backbone, allowing the backbone to be fine-tuned solely through a next-token prediction loss on both discretized actions and general language tokens, while the action expert is independently trained using flow-matching on continuous actions.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper conducts thorough experiments to demonstrate the importance of co-training in improving the language following ability of VLA. The experiments are challenging, the performance of the model is astonishing.\n2. The authors propose a novel training recipe, termed as knowledge insulation, which effectively mitigates gradient interference between the VLM backbone and the action expert in naive co-training strategies.\n\nWeaknesses:\n\n1. The training data used in the experiments is outlined in the paper, but further clarification or elaboration may be beneficial.\n\n2. The concept of knowledge insulation is intriguing, and similar phenomena have been explored in prior work. For example, ChatVLA[1] identifies that training exclusively on robot data causes the VLA to lose its multimodal capabilities entirely after fine-tuning, and it proposes a method for unifying multimodal understanding with robot control. ChatVLA-2[2] enables the VLA to preserve pretrained knowledge from the VLM while following reasoning in manipulation tasks. A discussion on how this approach relates to and differs from these works would be valuable.\n\n[1] ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model\n[2] ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge\n\nQUESTIONS:\n1. Are reasoning language tokens generated at test time? \n2. The example of reasoning is missing in the demo. Can you provide some demo samples includes the reasoning generated during inference?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates the integration of continuous action prediction and knowledge perservation of pretrained VLM. They demonstrates current approach significantly harms both training speed and knowledge transfer. This paper provides an extensive analysis of various design choices, their impact on performance and knowledge transfer, and propose a technique for insulating the VLM backbone during VLA training that mitigates this issue.\n\nSTRENGTHS AND WEAKNESSES:\npros:\n\n1) This paper investigates an interesting question: the integration of continuous action prediction and vision-language ability.\n2) This paper demonstrates a clean improvement compared to the recent state-of-the-art pi0.\n3) The robot experiments are extensive and solid.\n\ncons:\nThe paper claims the proposed approach achieves better knowledge insulting for VLA models from VLM. However, the evaluation is mainly based on manipulation performance according to experience. Meanwhile, this paper does not quantitatively demonstrate the knowledge preservation of VLM, e.g., evaluating it on the corresponding benchmarks.\n\nOverall, this paper is good and expands the knowledge boundary of vision-language-action models.\n\nQUESTIONS:\n1. L80-L81, \"The core idea of VLAs is to fine-tune pre-trained vision-language models (VLMs) for action prediction.\", which is very confusing.  Do you think VLA is limited to the finetuned VLM? How about the VLA models from scratch? I think it is also possible to jointly train the VLA model with both vlm dataset and the robot control data from scratch. Therefore, L80-L81 is quite confusing. Currently, the major attention of VLA lies on fine-tuning a VLM.\n2. This paper proposes to stop the gradient propagation from the continuous action expert, together with the discretized actions for gradient propagation. Did you have evaluate the training scheme without the discretized actions but with VLM supervisions? I found the text state refer to the discretized actions, and thus did not figure out which ablation is related to the experiment without discretized actions.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-08"}