Practice Makes Perfect: From Explicit Decomposition to Reinforced Latent Planning in Text-to-Human Motion

Recent work has shown that Chain-of-Thought (CoT) rea-soning improves text-to-motion generation, yet generating explicit rea-soning tokens at inference time introduces significant latency. We presentLaCT-Motion (Latent Chain-of-Thought for Motion), the first methodto transfer latent reasoning from language to LLM based human mo-tion generation, achieving explicit-CoT-level quality without its infer-ence overhead. Our three-stage recipe mirrors human skill acquisition:practice learns explicit CoT reasoning via supervised fine-tuning; in-ternalization progressively compresses variable-length reasoning stepsinto compact continuous latent tokens through a stage-based curricu-lum with stochastic scheduling; and perfection refines the fully latentpolicy with GRPO using format, motion similarity, and semantic simi-larity rewards. At inference, the model reasons entirely through latenttokens with no explicit text generated. Experiments on HumanML3Dshow that latent internalization not only matches but surpasses explicitCoT reasoning (R-Precision Top-1: 0.577 vs. 0.515 for Motion-R1, FID:0.167), suggesting that continuous latent tokens encode spatio-temporalreasoning more effectively than discrete text. Code will be released athttps://github.com/Erwin2233/LaCT-Motion.