Leveraging temporal context is crucial for success in partially observable robotic tasks. However, prior work in behavior cloning has demonstrated inconsistent performance gains when using multi-frame observations. In this paper, we introduce ContextVLA, a policy model that robustly improves robotic task performance by effectively leveraging multi-frame observations. Our approach is motivated by the key observation that Vision-Language-Action models (VLA), i.e., policy models built upon a Vision-Language Model (VLM), more effectively utilize multi-frame observations for action generation. This suggests that VLMs' inherent temporal understanding capability enables them to extract more meaningful context from multi-frame observations. However, the high dimensionality of video inputs introduces significant computational overhead, making VLA training and inference inefficient. To address this, ContextVLA compresses past observations into a single context token, allowing the policy to efficiently leverage temporal context for action generation. Our experiments show that ContextVLA consistently improves over single-frame VLAs and achieves the benefits of full multi-frame training but with reduced training and inference times. Many robotic tasks are inherently non-Markovian, i.e., the optimal decision at a given timestep t cannot be determined from the latest observation o For instance, an object may become occluded during manipulation (Shi et al., 2025). Solving long-horizon tasks may also require context about the previous motions of a robot, and handling dynamic environments often involves tracking the motion trajectories of moving objects (Zhang et al., 2025; Nasiriany et al., 2024).