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Enhancing Localized Reasoning for Long Video Understanding via Efficient Segment-to-Video Supervision

This paper proposes Segment-to-Video Supervision (S2V), an efficient training method that generates fine-grained question-answer pairs from localized video segments to enhance long video understanding in Multimodal Large Language Models, achieving superior accuracy and efficiency compared to existing reasoning-based approaches while avoiding the high costs and latency of complex reinforcement fine-tuning frameworks.

Original authors: Beibei Zhang, Chao Xu, Jun Lan, Zongyi Li, Lai Wei, Huijia Zhu, Tongwei Ren

Published 2026-08-24

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CC BY 4.0

Original authors: Beibei Zhang, Chao Xu, Jun Lan, Zongyi Li, Lai Wei, Huijia Zhu, Tongwei Ren

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

In the vast landscape of artificial intelligence, a specific challenge has long stood out: teaching machines to understand long videos. While modern systems can easily describe a short clip of a cat playing a piano, they often stumble when faced with a feature-length film or a two-hour documentary. The problem is not a lack of memory, but a lack of focus. When a computer analyzes a long video, it is bombarded with thousands of frames, most of which are irrelevant to the specific question being asked. This flood of visual information acts like static noise, drowning out the small, crucial details needed to find the answer. To solve this, researchers have tried to build systems that "think" harder, forcing them to search through the video step-by-step like a detective. However, these complex thinking processes are expensive to train and slow to run, often requiring massive amounts of data and time to produce a single answer.

A new approach, developed by researchers at Nanjing University and Ant Group, offers a different path. Instead of forcing the machine to stare at the entire video and guess where the answer might be, they teach it to look at small, manageable pieces first. The team created a method called Segment-to-Video Supervision. Imagine trying to find a specific word in a thick book. If you are told to read the whole book to find it, you might get lost in the story. But if you are shown a single page where the word appears, you learn exactly what the word looks like and where it sits. The researchers applied this logic to video. They took long videos and broke them down into short, distinct scenes. They then asked a powerful computer model to generate questions and answers based only on these short scenes. Because the scenes were short, the model could easily spot fine details, like the exact weight on a scale or the specific order of three actions, without getting distracted by the rest of the video.

Once the model learned to answer questions correctly based on these short clips, the researchers did something clever. They took those same questions and answers and presented them to the model again, but this time, they showed the entire long video. They added a simple instruction telling the model which part of the long video contained the answer. This forced the model to connect the sharp, clear understanding it had of the small scene with the noisy, complex reality of the full video. The goal was to train the model to ignore the distracting background and focus only on the relevant evidence, even when it was buried deep within hours of footage. The training process was surprisingly efficient. The team used only 10,000 of these specially crafted question-and-answer pairs to teach the model, a tiny fraction of the hundreds of thousands of examples other methods require. They also avoided complex, multi-step reasoning loops, allowing the model to give its answer in a single, quick pass.

The results of this approach were striking. When tested on various benchmarks designed to measure long video understanding, the new model consistently outperformed both general-purpose artificial intelligence systems and other specialized video models. It proved particularly strong at tasks requiring precise observation, such as counting objects or recalling the exact sequence of events in a long narrative. Unlike other systems that might take a long time to "think" through a problem or require massive computing power, this model delivered accurate answers quickly and with far less training data. The researchers found that the model learned to distinguish between useful details and irrelevant noise so well that it could answer questions correctly even when the specific time-stamp instructions were removed during testing. This suggests the model had genuinely learned to find the needle in the haystack, rather than just memorizing the location of the needle. By shifting the focus from looking at more data to looking at the right data in the right way, this work demonstrates a more efficient and effective way to teach machines to understand the complex, detailed stories told by long videos.

Technical Summary: Enhancing Localized Reasoning for Long Video Understanding via Efficient Segment-to-Video Supervision

Problem Statement

While Multimodal Large Language Models (MLLMs) have demonstrated significant potential in video understanding, Long Video Understanding (LVU) remains a critical challenge. The primary obstacle is the presence of distracting noise within complex and lengthy contexts, which obscures localized details and misleads MLLMs into generating incorrect answers.

Existing solutions attempt to mitigate this through two main approaches, both of which suffer from significant drawbacks:

Retrieval-based methods: These rely on external retrieval models to identify query-relevant evidence before feeding it to the MLLM. This introduces extra training and inference overheads, along with substantial computational and storage burdens.
Reasoning-based methods: Inspired by models like DeepSeek-R1, these leverage a Reinforcement Fine-Tuning (RFT) framework involving Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). However, they face two major issues:
- High Training Costs: The SFT phase requires large-scale Chain-of-Thought (CoT) annotations (often generated by expensive models like Gemini), and the RL phase necessitates complicated, task-specific reward designs.
- High Inference Latency: Some methods employ self-reflective and iterative-perception mechanisms, resulting in lengthy outputs (e.g., >1k tokens) and slow inference, which is a bottleneck for real-world applications.

Methodology: Segment-to-Video Supervision (S2V)

The authors propose S2V, a novel and efficient method designed to enhance fine-grained reasoning in LVU without the heavy costs of existing approaches. The core philosophy is to generate training data based on short, localized video segments and then transfer this supervision back to the whole video.

1. Data Synthesis Pipeline (S2V-10K)

The authors construct a fully automated pipeline to generate the S2V-10K dataset, consisting of 10,000 high-quality training samples:

Video Collection: Utilizing the YT-Temporal-180M corpus, long-duration videos are retained and segmented based on visual scene transitions to preserve semantic continuity.
Segment-based VQA Generation: A powerful MLLM ( $M_T$ ) generates multiple-choice Question-Answer (VQA) triplets based on short segments or combinations of up to $N_M$ segments. To ensure quality, a second MLLM ( $M'_T$ ) re-answers the questions; inconsistent pairs are discarded.
Transfer to Whole Video: To resolve referential ambiguity when applying segment-based questions to the full video, segment timestamps are explicitly included in the task instructions. This transforms segment-based VQAs into unambiguous video-based VQAs conditioned on specific segments.
Difficulty Filtering: A smaller MLLM ( $M_I$ ) responds to samples multiple times to categorize them into Simple, Medium, and Hard difficulties. The final dataset balances these categories (1:3:6 ratio) to ensure both convergence and challenge.

2. Training Strategy

Unlike RFT frameworks that require two-stage training (SFT + RL) and complex reward designs, S2V employs a streamlined approach:

Single-Stage RL: The model is trained using only Reinforcement Learning (specifically Group Relative Policy Optimization, GRPO).
Simple Reward: The reward function is a simple accuracy reward (1 for correct, 0 for incorrect).
Privileged Information: Segment timestamps act as "privileged information" during training to guide the model to associate fine-grained details with the correct answer. Crucially, the paper notes that once trained, the model does not require these timestamps during inference to perform well, as it has learned to perceive localized evidence independently.

3. Inference

S2V models predict answers using a single forward pass with limited output tokens, avoiding the iterative, multi-round generation processes that cause high latency in other reasoning-based methods.

Key Contributions

Novel Supervision Method: The proposal of Segment-to-Video Supervision (S2V), which enforces MLLMs to comprehend localized details from distracting long contexts by transferring segment-based annotations to the whole video.
Automated Data Pipeline & Dataset: The design of a fully automated pipeline to synthesize robust supervision data, resulting in the S2V-10K dataset, which covers needle-in-a-haystack problems and multi-segment reasoning.
Efficiency and Performance: Demonstrating that S2V significantly reduces training and inference costs (using only 10K samples and simple RL) while achieving superior accuracy compared to both general MLLMs and specialized LVU methods.

Experimental Results

Extensive experiments were conducted on three major LVU benchmarks: LongVideoBench (LVB), Video-MME, and MLVU.

Performance vs. General MLLMs: S2V models (4B, 7B, and 8B parameters) consistently outperformed state-of-the-art general MLLMs (e.g., Qwen3-VL, InternVL3.5) across all benchmarks. Notably, S2V showed more pronounced gains on medium- and long-video subsets.
Performance vs. LVU-Tailored Methods: S2V surpassed both retrieval-based and reasoning-based LVU methods.
- Data Efficiency: Compared to Video-R1, S2V reduced training data from 425K to 10K samples (98% reduction) and GPU hours from ~440 to ~160 (64% reduction).
- Accuracy: S2V-8B achieved competitive or superior accuracy to methods relying on much larger datasets and complex training pipelines.
Inference Efficiency: S2V achieved a better accuracy-latency trade-off. By avoiding iterative perception, it enables accurate answers in a single forward pass, significantly reducing inference latency compared to reasoning-based baselines.
Fine-Grained Reasoning: Ablation studies confirmed that S2V enhances the model's ability to perceive and utilize fine-grained evidence. When tested on videos with relevant segments removed, S2V models showed a larger performance drop than base models, indicating a stronger reliance on localized evidence.

Significance and Claims

The paper claims that S2V addresses the substantial training and inference overheads inherent in existing reasoning-based LVU methods. By shifting the focus from "looking more" (dense inputs) to "looking better" (fine-grained evidence association), S2V enables MLLMs to effectively distinguish useful evidence from distracting noise in long contexts.

The authors emphasize that S2V is not merely a data filtering technique but a paradigm shift in supervision. It proves that high-quality LVU performance can be achieved with:

Minimal Data: Only 10K carefully synthesized samples.
Simple Training: Single-stage RL with a basic accuracy reward.
Low Latency: Single-pass inference.

The work suggests that the key to improving LVU lies in training models to associate fine-grained, localized details with answers, rather than relying on expensive CoT annotations or iterative reasoning mechanisms.