ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent
  Planning
Authors: name: Chi-Pin Huang; name: Yueh-Hua Wu; name: Min-Hung Chen; name: Yu-Chiang Frank Wang; name: Fu-En Yang
Published: 2025-07-22 17:59:46

Abstract: Vision-language-action (VLA) reasoning tasks require agents to interpret
multimodal instructions, perform long-horizon planning, and act adaptively in
dynamic environments. Existing approaches typically train VLA models in an
end-to-end fashion, directly mapping inputs to actions without explicit
reasoning, which hinders their ability to plan over multiple steps or adapt to
complex task variations. In this paper, we propose ThinkAct, a dual-system
framework that bridges high-level reasoning with low-level action execution via
reinforced visual latent planning. ThinkAct trains a multimodal LLM to generate
embodied reasoning plans guided by reinforcing action-aligned visual rewards
based on goal completion and trajectory consistency. These reasoning plans are
compressed into a visual plan latent that conditions a downstream action model
for robust action execution on target environments. Extensive experiments on
embodied reasoning and robot manipulation benchmarks demonstrate that ThinkAct
enables few-shot adaptation, long-horizon planning, and self-correction
behaviors in complex embodied AI tasks.