Given a line-art sketch and user-specified color intent, controllable colorization must assign intended colors to fine-grained regions while preserving the underlying line structure. Existing methods have improved visual quality, but precise local adjustment remains difficult. In this work, we argue that the key limitation is the absence of a persistent correction state. Such a state records user intent, localized mismatches, grounded edit regions, and preservation constraints to guide targeted repairs. To address this, we introduce PaletteAgent, an agentic, self-evolving correction framework for controllable line-art colorization. PaletteAgent translates user intent into structured color prompts, employs judgment agents to diagnose mismatches, grounds problematic regions, preserves correct fields, and adaptively decides whether to refine globally, repair locally, or stop. This iterative process naturally defines the GRPO training objective for optimizing the agent’s correction policy. Empirical results show that PaletteAgent outperforms line-art colorization and general image-editing baselines. Further ablations confirm the role of maintaining a persistent correction state for controllable colorization.

Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions lead to high rewards, but provides little supervision on what those actions do to the environment. World modeling (WM) can fill this gap, yet existing approaches often require separate simulators, extra training stages, or additional inference-time computation. We observe that on-policy RL rollouts already contain the needed signal: each transition pairs an action with its resulting next observation. Based on this observation, we propose PaW, a Policy and World modeling co-training framework that adds auxiliary WM supervision to the same policy during RL, without changing the inference paradigm. To make auxiliary WM supervision informative and stable, PaW introduces three components: action-entropy-based WM data selection, noise-tolerant WM loss, and reward-adaptive loss balancing. Experiments on three agentic task benchmarks show consistent improvements over strong RL baselines across models and RL algorithms. These results suggest that standard RL rollouts are a practical source of WM supervision for language-agent training.