Multimodal Reward Hacking in Reinforcement Learning
This risk is amplified when visual evidence is evaluated by text-only or weakly grounded rewards. We study reward hacking in MLLM RL across safety VQA, chart VQA, and stress-test settings, varying reward design, data ambiguity, model scale (2B-32B), and RL algorithm (GRPO, RLOO, DAPO). We introduce Newly Rewarded Failure Rate (NRFR), which measures failures among samples whose proxy reward improves over the SFT baseline.
- ▪This risk is amplified when visual evidence is evaluated by text-only or weakly grounded rewards.
- ▪We study reward hacking in MLLM RL across safety VQA, chart VQA, and stress-test settings, varying reward design, data ambiguity, model scale (2B-32B), and RL algorithm (GRPO, RLOO, DAPO).
- ▪We introduce Newly Rewarded Failure Rate (NRFR), which measures failures among samples whose proxy reward improves over the SFT baseline.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2607.09492 |
| Publication time | Mon, 13 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-13T04:20:37.625Z |
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Computer Science > Artificial Intelligence arXiv:2607.09492 (cs) [Submitted on 10 Jul 2026] Title:Multimodal Reward Hacking in Reinforcement Learning Authors:Jiayu Yao, Yiwei Wang, Anmeng Zhang, Zhe Sun, Songsong Wang, Lingrui Mei, Yuyao Ge, Shenghua Liu View a PDF of the paper titled Multimodal Reward Hacking in Reinforcement Learning, by Jiayu Yao and 7 other authors View PDF HTML (experimental) Abstract:Reinforcement learning (RL) is increasingly used to align multimodal large language models (MLLMs), but higher rewards do not always imply better task performance. This risk is amplified when visual evidence is evaluated by text-only or weakly grounded rewards.
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