Controlling physics-based humanoids from natural-language instructions is a critical step toward general-purpose embodied agents. However, existing methods remain constrained by a tension between semantic expressiveness and physical feasibility, often failing to jointly achieve faithful instruction following, high-quality motion, and stable long-horizon control. We propose SCRIPT, a scalable diffusion policy with a multi-stage training framework for language-driven physics-based humanoid control. The core of SCRIPT is a Joint Action-State-Text Diffusion Transformer (JAST-DiT), which represents actions, physical states, and text as dedicated token streams and couples them through joint attention, enabling direct interaction between language semantics and control dynamics. To stabilize autoregressive control, we introduce a nonlinear history conditioning mechanism, which preserves the dense recent context and samples increasingly sparse cues from long-term history. Beyond supervised imitation pre-training, we propose a post-training stage, further improving the performance using Reinforcement Learning with Hybrid Rewards (RLHR). By injecting learnable noise into the flow-sampling process, RLHR effectively improves motion quality and instruction following within closed-loop simulations using hybrid physical feedback and text rewards. Quantitative evaluations demonstrate that SCRIPT outperforms prior state-of-the-art methods, with gains across text alignment, motion quality, and physical realism metrics. Furthermore, scaling studies on the 1200-hour MotionMillion dataset demonstrate consistent performance gains with model scaling, highlighting SCRIPT's robust scalability for large-scale pre-training. Our code will be publicly available for future research.

翻译：从自然语言指令控制物理仿真人体是迈向通用具身智能体的关键步骤。然而，现有方法仍受限于语义表达力与物理可行性之间的张力，往往难以同时实现忠实指令跟随、高质量运动与稳定长时域控制。我们提出SCRIPT——一种可扩展的扩散策略，结合多阶段训练框架用于语言驱动的物理仿真人体控制。SCRIPT的核心是联合动作-状态-文本扩散Transformer（JAST-DiT），该模型将动作、物理状态与文本编码为专用词元流，并通过联合注意力机制实现跨模态耦合，使语言语义与控制动力学直接交互。为稳定自回归控制，我们引入非线性历史条件机制：保留密集的近期上下文，并从长期历史中采样逐渐稀疏的线索。除监督式模仿预训练外，我们提出后训练阶段，通过混合奖励强化学习进一步提升性能。通过向流采样过程注入可学习噪声，RLHR利用混合物理反馈与文本奖励有效提升闭环仿真中的运动质量与指令跟随能力。定量评估表明，SCRIPT在文本对齐、运动质量与物理真实性指标上均超越现有最优方法。此外，基于1200小时MotionMillion数据集的扩展性研究显示，模型扩展能持续带来性能提升，突显SCRIPT在大规模预训练中的稳健可扩展性。我们的代码将公开供后续研究使用。