MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for Video Temporal Reasoning
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915515858944000 |
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| author | Tao, Sicheng Li, Jungang Yan, Yibo Zhang, Junyan Gao, Yubo Li, Hanqian Xun, ShuHang Fan, Yuxuan Chen, Hong He, Jianxiang Hu, Xuming |
| author_facet | Tao, Sicheng Li, Jungang Yan, Yibo Zhang, Junyan Gao, Yubo Li, Hanqian Xun, ShuHang Fan, Yuxuan Chen, Hong He, Jianxiang Hu, Xuming |
| contents | Video reasoning has emerged as a critical capability for multimodal large language models (MLLMs), requiring models to move beyond static perception toward coherent understanding of temporal dynamics in complex scenes. Yet existing MLLMs often exhibit process inconsistency, where intermediate reasoning drifts from video dynamics even when the final answer is correct, undermining interpretability and robustness. To address this issue, we introduce MOSS-ChatV, a reinforcement learning framework with a Dynamic Time Warping (DTW)-based process reward. This rule-based reward aligns reasoning traces with temporally grounded references, enabling efficient process supervision without auxiliary reward models. We further identify dynamic state prediction as a key measure of video reasoning and construct MOSS-Video, a benchmark with annotated reasoning traces, where the training split is used to fine-tune MOSS-ChatV and the held-out split is reserved for evaluation. MOSS-ChatV achieves 87.2\% on MOSS-Video (test) and improves performance on general video benchmarks such as MVBench and MMVU. The framework consistently yields gains across different architectures, including Qwen2.5-VL and Phi-2, confirming its broad applicability. Evaluations with GPT-4o-as-judge further show that MOSS-ChatV produces more consistent and stable reasoning traces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for Video Temporal Reasoning Tao, Sicheng Li, Jungang Yan, Yibo Zhang, Junyan Gao, Yubo Li, Hanqian Xun, ShuHang Fan, Yuxuan Chen, Hong He, Jianxiang Hu, Xuming Computer Vision and Pattern Recognition Video reasoning has emerged as a critical capability for multimodal large language models (MLLMs), requiring models to move beyond static perception toward coherent understanding of temporal dynamics in complex scenes. Yet existing MLLMs often exhibit process inconsistency, where intermediate reasoning drifts from video dynamics even when the final answer is correct, undermining interpretability and robustness. To address this issue, we introduce MOSS-ChatV, a reinforcement learning framework with a Dynamic Time Warping (DTW)-based process reward. This rule-based reward aligns reasoning traces with temporally grounded references, enabling efficient process supervision without auxiliary reward models. We further identify dynamic state prediction as a key measure of video reasoning and construct MOSS-Video, a benchmark with annotated reasoning traces, where the training split is used to fine-tune MOSS-ChatV and the held-out split is reserved for evaluation. MOSS-ChatV achieves 87.2\% on MOSS-Video (test) and improves performance on general video benchmarks such as MVBench and MMVU. The framework consistently yields gains across different architectures, including Qwen2.5-VL and Phi-2, confirming its broad applicability. Evaluations with GPT-4o-as-judge further show that MOSS-ChatV produces more consistent and stable reasoning traces. |
| title | MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for Video Temporal Reasoning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.21113 |