TIR-Agent: Training an Explorative and Efficient Agent for Image Restoration

Vision-language agents that orchestrate specialized tools forimage restoration (IR) have emerged as a promising method, yet most ex-isting frameworks operate in a training-free manner. They rely on heuris-tic task scheduling and exhaustive tool traversal, resulting in sub-optimalrestoration paths and prohibitive computational cost. We argue that thecore bottleneck lies in the absence of a learned policy to make decision,as a vision-language model cannot efficiently handle degradation-awaretask ordering and tool composition. To this end, we propose TIR-Agent, atrainable image restoration agent that performs a direct tool-calling pol-icy through a two-stage training pipeline of supervised fine-tuning (SFT)followed by reinforcement learning (RL). Two key designs underpin effec-tive RL training: (i) a random perturbation strategy applied to the SFTdata, which broadens the policy’s exploration over task schedules andtool compositions, and (ii) a multi-dimensional adaptive reward mech-anism that dynamically re-weights heterogeneous image quality metricsto mitigate reward hacking. To support high-throughput, asynchronousGPU-based tool invocation during training, we further develop a glob-ally shared model-call pool. Experiments on both in-domain and out-of-domain degradations show that TIR-Agent outperforms 12 baselines,including 6 all-in-one models, 3 training-free agents, and 3 proprietarymodels, and achieves over 2.5× inference speedup by eliminating redun-dant tool executions.