A self-improving multimodal model can decide when to switch to fresh problems using only its training logs 🔄 A-RSI: Adaptive Task Refresh for Zero-Data Multimodal Self-Improvement 📌 Overview A-RSI is a method for refreshing the training task pool at the right time when a VLM improves itself without human labels. ⚠️ The problem ・Most work focuses on what to generate next, not when to replace the data ・Labels from majority voting can turn the model's own errors into training targets 🧩 Method ・The base model writes parameterized task generators that programmatically produce an image, a question, and a reference answer ・Training uses GRPO, with correctness ranked above all auxiliary rewards ・When prompts with reward variance drop or accuracy rises, the whole pool is replaced with unused tasks 📊 Results ・Qwen3-VL-8B's 11-benchmark average rose from 57.77 to 61.05 using 800 fresh tasks ・Refreshing every window reached 60.64 but used 3,200 tasks, four times as many ・MiMo-VL-7B also went from 59.08 to 60.71 🔭 Why it matters Timing the refresh without validation scores is practical. Note that each comparison is a single training run. #SelfImprovement# #MultimodalAI#