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Beyond Noise Steering: Dual-Latent Space Reinforcement Learning for Generative Robot Policy

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

Pretrained generative robot policies learn expressive action priors from demonstrations. However, existing reinforcement learning methods only steer the noisy space but fail to modulate intermediate action representations during the generation process, resulting in performance degradation and inefficiency. To address this limitation, we propose a novel Dual-Latent Space Reinforcement Learning (DLSRL) framework, which complements initial-noise steering with representation-level control inside the frozen generator. Specifically, our actor network predicts two distinct latent variables: an initia

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