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This paper introduces TTA-Vid, a test-time adaptation approach for video reasoning that leverages reinforcement learning to adapt a pre-trained model to new video samples without labels. TTA-Vid uses step-by-step reasoning on frame subsets and a batch-aware frequency-based reward as pseudo-ground truth to update the model during inference. The results demonstrate that TTA-Vid generalizes well across datasets and outperforms state-of-the-art methods trained on large-scale labeled data, highlighting the potential of test-time RL for temporal multimodal understanding.
Forget finetuning: TTA-Vid adapts video reasoning models to new datasets *during inference* using test-time reinforcement learning, achieving state-of-the-art results without any labels.
Recent video reasoning models have shown strong results on temporal and multimodal understanding, yet they depend on large-scale supervised data and multi-stage training pipelines, making them costly to train and difficult to adapt to new domains. In this work, we leverage the paradigm of Test-Time Reinforcement Learning on video-language data to allow for adapting a pretrained model to incoming video samples at test-time without explicit labels. The proposed test-time adaptation for video approach (TTA-Vid) combines two components that work simultaneously: (1) a test-time adaptation that performs step-by-step reasoning at inference time on multiple frame subsets. We then use a batch-aware frequency-based reward computed across different frame subsets as pseudo ground truth to update the model. It shows that the resulting model trained on a single batch or even a single sample from a dataset, is able to generalize at test-time to the whole dataset and even across datasets. Because the adaptation occurs entirely at test time, our method requires no ground-truth annotations or dedicated training splits. Additionally, we propose a multi-armed bandit strategy for adaptive frame selection that learns to prioritize informative frames, guided by the same reward formulation. Our evaluation shows that TTA-Vid yields consistent improvements across various video reasoning tasks and is able to outperform current state-of-the-art methods trained on large-scale data. This highlights the potential of test-time reinforcement learning for temporal multimodal understanding.
