DualVLA: Building a Generalizable Embodied Agent via Partial Decoupling of Reasoning and Action
This addresses the challenge of building generalizable embodied agents for robotics by balancing reasoning and action execution, though it is incremental as it builds on existing VLA strategies.
The paper tackles the problem of action degeneration in generalizable Vision-Language-Action models, where reasoning capabilities degrade action performance, and proposes DualVLA to enhance action performance while preserving reasoning, achieving an average success rate of 61.0 in SimplerEnv and 65.4 across eight multimodal benchmarks.
To build a generalizable Vision-Language-Action (VLA) model with strong reasoning ability, a common strategy is to first train a specialist VLA on robot demonstrations to acquire reliable manipulation skills, and then incorporate mixed annotated robot data together with multimodal data to restore broader reasoning capabilities. However, we observe that the resulting reasoning VLA often suffers from degraded action performance compared to the specialist model before fine-tuning, a phenomenon we refer to as action degeneration. To address this issue, we propose DualVLA, which enhances action performance through carefully designed post-training while still preserving reasoning capability. We first introduce a dual-layer data pruning method that removes redundant embodied reasoning, preventing it from adversely influencing action learning. To further strengthen action generation, we design a dual-teacher adaptive distillation strategy that assigns different supervision signals to different data domains while maintaining reasoning ability. To fill the evaluation gap for generalist VLAs, we also propose VLA Score, which decouples VLA capability into reasoning, intention, action, and alignment dimensions for a more fine-grained assessment. Experiments show that DualVLA achieves an average success rate of 61.0 in SimplerEnv and an average score of 65.4 across eight competitive multimodal benchmarks, demonstrating a stronger balance between precise action execution and multimodal understanding. Project Website: https://costaliya.github.io/DualVLA/.