VideoSSR: Video Self-Supervised Reinforcement Learning
Reinforcement Learning with Verifiable Rewards (RLVR) has substantially
advanced the video understanding capabilities of Multimodal Large Language
Models (MLLMs). However, the rapid progress of MLLMs is outpacing the
complexity of existing video datasets, while the manual annotation of new,
high-quality data remains prohibitively expensive. This work investigates a
pivotal question: Can the rich, intrinsic information within videos be
harnessed to self-generate high-quality, verifiable training data? To
investigate this, we introduce three self-supervised pretext tasks: Anomaly
Grounding, Object Counting, and Temporal Jigsaw. We construct the Video
Intrinsic Understanding Benchmark (VIUBench) to validate their difficulty,
revealing that current state-of-the-art MLLMs struggle significantly on these
tasks. Building upon these pretext tasks, we develop the VideoSSR-30K dataset
and propose VideoSSR, a novel video self-supervised reinforcement learning
framework for RLVR. Extensive experiments across 17 benchmarks, spanning four
major video domains (General Video QA, Long Video QA, Temporal Grounding, and
Complex Reasoning), demonstrate that VideoSSR consistently enhances model
performance, yielding an average improvement of over 5\%. These results
establish VideoSSR as a potent foundational framework for developing more
advanced video understanding in MLLMs. The code is available at
https://github.com/lcqysl/VideoSSR.
6912a658a644ba07c499c6f5
Zefeng He
6912a658a644ba07c499c6f6
Xiaoye Qu
6912a658a644ba07c499c6f7
Yafu Li
6912a658a644ba07c499c6f8
Siyuan Huang
claimed_verified
2026-04-01T08:37:29.201Z
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/avatars/92918bf8913012a3f005f09e03b381c2.svg
Siyuan Huang
chamber111
user
chamber111
6912a658a644ba07c499c6f9
Daizong Liu
6912a658a644ba07c499c6fa
Yu Cheng
VideoSSR: Video Self-Supervised Reinforcement Learning
Reinforcement Learning with Verifiable Rewards (RLVR) has substantially
advanced the video understanding capabilities of Multimodal Large Language
Models (MLLMs). However, the rapid progress of MLLMs is outpacing the
complexity of existing video datasets, while the manual annotation of new,
high-quality data remains prohibitively expensive. This work investigates a
pivotal question: Can the rich, intrinsic information within videos be
harnessed to self-generate high-quality, verifiable training data? To
investigate this, we introduce three self-supervised pretext tasks: Anomaly
Grounding, Object Counting, and Temporal Jigsaw. We construct the Video
Intrinsic Understanding Benchmark (VIUBench) to validate their difficulty,
revealing that current state-of-the-art MLLMs struggle significantly on these
tasks. Building upon these pretext tasks, we develop the VideoSSR-30K dataset
and propose VideoSSR, a novel video self-supervised reinforcement learning
framework for RLVR. Extensive experiments across 17 benchmarks, spanning four
major video domains (General Video QA, Long Video QA, Temporal Grounding, and
Complex Reasoning), demonstrate that VideoSSR consistently enhances model
performance, yielding an average improvement of over 5\%. These results
establish VideoSSR as a potent foundational framework for developing more
advanced video understanding in MLLMs. The code is available at
https://github.com/lcqysl/VideoSSR.
A novel video self-supervised reinforcement learning framework, VideoSSR, enhances MLLM performance across various video understanding tasks by leveraging intrinsic video information.
Reinforcement Learning with Verifiable Rewards Multimodal Large Language Models self-supervised pretext tasks Anomaly Grounding Object Counting Temporal Jigsaw Video Intrinsic Understanding Benchmark VideoSSR-30K dataset VideoSSR General Video QA Long Video QA Temporal Grounding Complex Reasoning