MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for
  Video Temporal Reasoning
Authors: name: Sicheng Tao, name: Jungang Li, name: Yibo Yan, name: Junyan Zhang, name: Yubo Gao, name: Hanqian Li, name: ShuHang Xun, name: Yuxuan Fan, name: Hong Chen, name: Jianxiang He, name: Xuming Hu

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.