MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for
  Video Temporal Reasoning
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.
68d5feea8ccd91bdd39ffde8
Sicheng Tao
68d5feea8ccd91bdd39ffde9
Jungang Li
68d5feea8ccd91bdd39ffdea
Yibo Yan
68d5feea8ccd91bdd39ffdeb
Junyan Zhang
68d5feea8ccd91bdd39ffdec
Yubo Gao
68d5feea8ccd91bdd39ffded
Hanqian Li
68d5feea8ccd91bdd39ffdee
ShuHang Xun
68d5feea8ccd91bdd39ffdef
Yuxuan Fan
claimed_verified
2025-09-26T12:16:54.117Z
658247c592b5a9664de63882
https://cdn-avatars.huggingface.co/v1/production/uploads/658247c592b5a9664de63882/Jn03voLQjDlB3YjpSi-PI.jpeg
fan
user
EasonFan
68d5feea8ccd91bdd39ffdf0
Hong Chen
68d5feea8ccd91bdd39ffdf1
Jianxiang He
68d5feea8ccd91bdd39ffdf2
Xuming Hu
MOSS-ChatV: Reinforcement Learning with Process Reasoning Reward for
  Video Temporal Reasoning
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.
MOSS-ChatV, a reinforcement learning framework with a DTW-based reward, improves video reasoning consistency and performance across various benchmarks.
reinforcement learning Dynamic Time Warping (DTW) process reward reasoning traces dynamic state prediction MOSS-Video MVBench MMVU Qwen2.5-VL Phi-2 GPT-4o-as-judge