←Back to feed
🧠 AI🟢 BullishImportance 6/10
Visual-ERM: Reward Modeling for Visual Equivalence
arXiv – CS AI|Ziyu Liu, Shengyuan Ding, Xinyu Fang, Xuanlang Dai, Penghui Yang, Jianze Liang, Jiaqi Wang, Kai Chen, Dahua Lin, Yuhang Zang|
🤖AI Summary
Researchers introduce Visual-ERM, a multimodal reward model that improves vision-to-code tasks by evaluating visual equivalence in rendered outputs rather than relying on text-based rules. The system achieves significant performance gains on chart-to-code tasks (+8.4) and shows consistent improvements across table and SVG parsing applications.
Key Takeaways
- →Visual-ERM addresses reward hacking issues in vision-to-code reinforcement learning by providing fine-grained visual feedback.
- →The system improved Qwen3-VL-8B-Instruct performance by +8.4 on chart-to-code and +2.7, +4.1 on table and SVG parsing respectively.
- →A new benchmark VisualCritic-RewardBench was introduced to evaluate fine-grained image-to-image discrepancies on structured visual data.
- →Visual-ERM at 8B parameters outperformed much larger models like Qwen3-VL-235B-Instruct on the benchmark.
- →The research demonstrates that fine-grained visual reward supervision is sufficient for vision-to-code RL across different tasks.
#visual-erm#reward-modeling#vision-to-code#reinforcement-learning#multimodal-ai#computer-vision#lvlm#benchmark#qwen3-vl#arxiv
Read Original →via arXiv – CS AI
Act on this with AI
Stay ahead of the market.
Connect your wallet to an AI agent. It reads balances, proposes swaps and bridges across 15 chains — you keep full control of your keys.
Related Articles