VISA: Agentic Self-Evolving Data Synthesis for Multimodal Instruction Following
Score
72提出了自进化闭环数据合成新范式，突破了传统一次性生成-过滤的限制，信息增量明显。
方法设计完整，包含记忆回写、诊断恢复和奖励信号复用，实验覆盖强基线与多基准评估。
可降低多模态数据人工标注成本，并为强化学习提供奖励信号，具备实际落地潜力。
针对大模型训练数据稀缺与质量痛点，契合当前自监督与智能体研究热点。
来自arXiv预印本，方法描述详细且实验评估公开，但尚未经同行评审。
Base 79.5 × T2 Source weight ×0.9 = 72
Curation line 70Selected
Why selected
The method introduces closed-loop feedback and memory into data synthesis, showing strong innovation and practical potential, though broader cross-task generalization remains to be verified.
AI commentary
「VISA's value lies in turning data synthesis from a one-shot tool into an adaptive system that lets a model repeatedly refine its own weak spots, which is inspiring for building smarter multimodal assistants. Those working on data pipelines or RL training should read it closely, especially the design of using verifier signals as rewards.」
Quick overview
Multimodal instruction-following models require training data that is accurate, diverse, verifiable, and challenging, but existing synthesis pipelines usually follow a one-pass generate-and-filter paradigm, discarding feedback from failures. VISA proposes an agentic self-evolving loop: each round analyzes an image, filters incompatible constraints, samples diversity- and difficulty-aware constraint sets, generates candidate instructions, and verifies them with executable tools and structured LLM judges. Failed samples trigger diagnostic-guided recovery, accepted samples are probed against the target model for difficulty, and verifier signals plus failure profiles are written back to memory to adaptively expand the constraint space and focus on weaknesses. Experiments on MM-IFEval show VISA consistently improves multimodal instruction following over strong baselines while preserving general multimodal capability across seven public benchmarks.
- VISA formulates multimodal instruction synthesis as a self-evolving loop, dynamically leveraging failed samples, verifier outcomes, and target-model errors.
- Candidate instructions are verified with executable tools and structured LLM judges; failed samples trigger diagnostic-guided recovery.
- Verifier signals and target-model failure profiles are written back to memory to adaptively expand constraint space and focus on unresolved weaknesses.
- The verifier contracts provide reward signals for reinforcement learning without a separately trained reward model.
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