Beyond Needle(s) in the Embodied Haystack: Environment, Architecture, and Training Considerations for Long Context Reasoning

To enable this capability, we explore architectural adaptations, including interleaved Goal-State-Action modeling, context extension techniques, and Context Parallelism, to equip LLM-based agents for extreme long-context reasoning and interaction. Experimental results and analyses highlight the challenges posed by our benchmark and provide insights into training strategies and model behaviors under long-horizon conditions. Our work provides a foundation for the next generation of embodied AI systems capable of robust, long-term reasoning and planning. Real-world embodied reasoning is a sequential decision-making problem requiring long-horizon planning, where task success depends on both memorizing and reasoning over multiple events that occur far apart in time. Using large pre-trained vision-language-action (VLA) models as policies for such tasks requires surpassing the key challenge of long-context reasoning. We seek to answer questions pertaining to what design choices matter in terms of environments, model architectures, and training methods when using VLA models for long-horizon embodied tasks. To this end, we develop a new framework for long-horizon tasks designed to push the boundaries of long-context understanding in embodied AI. Our benchmark uniquely features tasks with a synthetic final goal, which involves multiple objects that appear at distant time steps, requiring multi-step reasoning across over hundreds of steps. Figure 1 illustrates an example: the agent observes the tomato at an early step (t=17) and the counter top much later (t=560). Then, the final task is given at t=670, which requires the agent to place the tomato on the counter top. This setup highlights the challenge of long-horizon dependency, where key objects and locations must be remembered and acted upon after hundreds of steps. Beyond these long-horizon dependencies, our framework also generates low-level robot-arm manipulation action sequences aligned with the trajectories, enabling agents to bridge from high-level reasoning to fine-grained physical execution.