RSIgen: Reciprocal Self-Improvement for Agentic Visual Generation
Agentic visual generation produces experience that can improve the native understanding (agent) and generation roles of a unified multimodal backbone, but task success alone does not determine which role should learn.
ProofPaper ↗
Key points
- Generator updates can also make the agent's action supervision stale.
- We introduce RSIgen, a task-centric reciprocal self-improvement framework linking learning allocation, supervision refresh, and recursive curriculum construction.
- After generator learning, RSIgen re-evaluates the relative utility of tested actions with branch inputs held fixed to refresh agent supervision; the retained roles then construct the next curriculum.
- Across BAGEL, Qwen-Image, and Wan-Image-7B, RSIgen improves AgentGen-Bench Full Overall9 by 3.2--3.3 points over a three-cycle SearchGen adaptation with action costs.
Sources (1)
- [1]RSIgen: Reciprocal Self-Improvement for Agentic Visual GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 04:41 PM
Agentic visual generation produces experience that can improve the native understanding (agent) and generation roles of a unified multimodal backbone, but task success alone does not determine which role should learn.
Generator updates can also make the agent's action supervision stale.
Extractive summary: sentences quoted from the sources.
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