OPPO applies RL with an Omni-Perception Reward and masked-input KL loss to boost cue utilization and suppress hallucinations in emotion reasoning MLLMs, claiming SOTA results on MER-UniBench, MME-Emotion, and MEP-Bench.
arXiv preprint arXiv:2509.21854 , year=
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UNVERDICTED 4representative citing papers
AutoSearch applies RL with a self-answering reward to adaptively determine minimal sufficient search depth in agentic RAG, reducing over-searching while maintaining answer quality on complex questions.
SRCP improves zero-shot generalization of successor representation methods in visual unsupervised reinforcement learning via saliency-guided representations and consistency policies.
OpenVLThinkerV2 applies a new Gaussian GRPO training objective with response and entropy shaping to outperform prior open-source and proprietary models on 18 visual reasoning benchmarks.
citing papers explorer
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Omni-Perception Policy Optimization for Multimodal Emotion Reasoning
OPPO applies RL with an Omni-Perception Reward and masked-input KL loss to boost cue utilization and suppress hallucinations in emotion reasoning MLLMs, claiming SOTA results on MER-UniBench, MME-Emotion, and MEP-Bench.
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AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning
AutoSearch applies RL with a self-answering reward to adaptively determine minimal sufficient search depth in agentic RAG, reducing over-searching while maintaining answer quality on complex questions.
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Saliency-Guided Representation with Consistency Policy Learning for Visual Unsupervised Reinforcement Learning
SRCP improves zero-shot generalization of successor representation methods in visual unsupervised reinforcement learning via saliency-guided representations and consistency policies.
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OpenVLThinkerV2: A Generalist Multimodal Reasoning Model for Multi-domain Visual Tasks
OpenVLThinkerV2 applies a new Gaussian GRPO training objective with response and entropy shaping to outperform prior open-source and proprietary models on 18 visual reasoning benchmarks.