Title: LongVPO: From Anchored Cues to Self-Reasoning for Long-Form Video Preference Optimization
Venue: NeurIPS 2025
Accepted: yes
Keywords: Video Large Language Models; Long Video Understanding; Preference Optimization
Abstract: We present LongVPO, a novel two‑stage Direct Preference Optimization framework that enables short‑context vision‑language models to robustly understand ultra‑long videos without any long‑video annotations. In Stage 1, we synthesize preference triples by anchoring questions to individual short clips, interleaving them with distractors, and applying visual‑similarity and question‑specificity filtering to mitigate positional bias and ensure unambiguous supervision. We also approximate the reference model’s scoring over long contexts by evaluating only the anchor clip, reducing computational overhead. In Stage 2, we employ a recursive captioning pipeline on long videos to generate scene-level metadata, and then use a large language model to craft multi-segment reasoning queries and dispreferred responses, aligning the model's preferences through multi-segment reasoning tasks. With only 16K synthetic examples and no costly human labels, \model{} outperforms the state‑of‑the‑art open‑source models on multiple long‑video benchmarks, while maintaining strong short‑video performance (e.g., on MVBench), offering a scalable paradigm for efficient long‑form video understanding.

LongVPO: From Anchored Cues to Self-Reasoning for Long-Form Video Preference Optimization

Abstract

We present LongVPO, a novel two-stage Direct Preference Optimization framework that enables short-context vision-language models to robustly understand ultra-long videos without any long-video annotations. In Stage 1, we synthesize preference triples by anchoring questions to individual short clips, interleaving them with distractors, and applying visual-similarity and question-specificity filtering to mitigate positional bias and ensure unambiguous supervision. We also approximate the reference model's scoring over long contexts by evaluating only the anchor clip, reducing computational overhead. In Stage 2, we employ a recursive captioning pipeline on long videos to generate scene-level metadata, then use a large language model to craft multi-segment reasoning queries and dispreferred responses, aligning the model's preferences through multi-segment reasoning tasks. With only 16K synthetic examples and no costly human labels, LongVPO outperforms the state-of-the-art open-source models on multiple long-video benchmarks, while maintaining strong short-video performance (e.g., on MVBench), offering a scalable paradigm for efficient long-form video understanding.

1 Introduction

Recent vision-language models (VLMs)[32, 8, 60, 7, 41, 25] have demonstrated impressive capabilities in both image and video understanding. However, their performance often degrades when applied to tasks that require long-context visual reasoning, such as analyzing videos that span over an hour [48, 14, 62]. This presents a significant challenge in scaling VLMs for long-form video understanding.

While recent progress in long-video VLMs [43, 40, 7] has been encouraging, most approaches rely heavily on costly, high-quality annotations for long videos, limiting their scalability in practical applications. In contrast, existing short-context VLMs-despite being trained only on limited-frame inputs-have shown surprisingly competitive results on long-video benchmarks, largely thanks to their strong foundational vision-language alignment. This observation suggests a promising direction: short-context VLMs may possess untapped potential for long-video modeling if properly extended. This raises a natural question: How far can we push short-context VLMs into the long-video regime-without the burden of expensive re-training or labels?

To explore this question, we start with a strong short-context VLM [8, 19] that was not trained with long-range visual inputs and evaluate its performance on long-video understanding tasks. We identify two key challenges that limit its effectiveness: (1) Scarcity of Long-Form Video Annotations:

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High-quality video-text annotations, such as detailed captions or question-answer pairs, are typically available only for short clips, where annotators can reasonably cover the content. For long videos spanning tens of thousands of frames, such annotation becomes prohibitively expensive [54] and often suffers from incomplete coverage and poor temporal alignment [47]. (2) Context-Length Bias in Short-Context VLMs: Common practices such as YARN [35], NTK can extend positional encodings for longer sequences, but the resulting models still suffer from position-related biases and limited performance gains. To simulate long-context scenarios, we embed the original short video's 1D frame sequence within a much longer sequence, padding the surrounding positions with blank frames (visualized as a grid in Fig. 1). This setup allows us to investigate the model's sensitivity to spatial positions across extended contexts. Specifically, by computing the L1 distance between each frame's position and a fixed query point, we uncover a 'lost-in-the-middle' phenomenon-analogous to what has been observed in long-sequence language models [3]-where the model's performance dips for inputs located near the center of the grid. This highlights a positional bias that disfavors centrally located content (see Fig. 1).

Recent efforts [23] attempt to address this by leveraging Direct Preference Optimization (DPO) to enhance grounding capabilities. However, as depicted in Fig. 2, this method assumes access to a reference model that already supports long-context reasoning. This assumption does not hold for short-context VLMs. Moreover, this approach requires proprietary models to generate and filter preference data, which introduces external language model biases without fully viewing the video. As a result, the method fails to fundamentally resolve the problem and delivers suboptimal performance.

To address these challenges, we propose a two-stage training framework that extends short-context VLMs to ultra-long video contexts, as shown in Fig. 3: Stage 1: Efficient Short-to-Long Learning from Anchored Cues. We form mixed, interleaved sequences of short clips from the SFT dataset. For each clip, we generate an anchor question and use the short-context VLM's answer as the Preferred Response, ensuring via scalable auto-filtering that each question refers to exactly one clip. The model learns to maximize the likelihood of the Preferred Response given the anchor question and its corresponding clip. To simulate distracting contexts, we introduce Dis-Preferred Responses by prompting temporally misaligned clips, forcing the model to retrieve the correct answer from many candidates. We also randomize the target clip's position within the sequence to mitigate positional biases during training. Stage 2: Self-Training for Long Video Preference Alignment. Building on Stage 1's memory and retrieval single-segment skills, we train the model to handle longer, more complex videos, without requiring ground-truth annotations. First, we employ a recursive captioning pipeline to generate structured, scene-level metadata to leverage the model's short-context capabilities. We then transfer insights from open-source LLMs' long-text understanding: given a sequence of scene-organized captions, we generate questions and identify the minimal set of scenes needed to answer them. We then craft Dis-Preferred Responses by prompting the model with partial or misleading context (e.g., omitting critical scenes), encouraging it to assemble the complete information chain required for accurate answers in real-world, long-video scenarios.

In summary, our contributions are threefold:

We introduce LongVPO, a two-stage framework that extends short-context VLMs to long video contexts without relying on any long-video annotations.

We construct a synthetic DPO training set from short visual context transfer to long text context for long videos, using only ~16k instances-significantly fewer than existing instructiontuning datasets -eliminating the need for long-video labels.

Our approach outperforms existing long-video models trained on large-scale supervised and preference-optimized data across challenging long-video understanding benchmarks, while maintaining competitive performance on short-video tasks, offering a new superior paradigm for efficient multimodal long video understanding.

2 Related work

Long-Context

Vision Language Model

Short-Context

Vision Language Model

Previous Methods

Policy Model

(short-context)

Long Video + Query

Policy Model

Mixed Interleaved Short

Clips + Query

Short Clip + Query

Ours - Stage 1

Policy Model

(long-context)

Ours - Stage 2

Short-Context

Vision Language Model

(short-context)

Long Video + Query

VLMs for Long Video Understanding. Advancements in vision-large models (VLMs) [8, 11, 21, 20, 26, 31, 60] have shown impressive capabilities in video understanding, and many models demonstrated excellent performance in short video analysis. Some recent works make further efforts towards long video understanding by designing certain strategies to compress/select visual context [10, 13, 17, 22, 40, 42] or extend the temporal context window [7, 41, 57]. In addition to the innovation in model architecture, it is also important to construct long-form video instruction datasets to guide VLMs in extracting detailed visual cues and modeling cross-temporal relationships. Representative methods such as VideoChatFlash [25], Kangaroo [27], and Video-XL [42] build data production pipelines and curate long video data to enhance the ability of video VLM. However, high-quality long video annotations can be expensive and time-consuming, making them difficult to obtain and scale up compared to short videos [6]. Therefore, developing an efficient strategy to employ short video-text data to facilitate VLMs in long video understanding remains a challenge. In this work, we propose a novel two-stage framework for VLMs to progressively learn the ability to analyze longer videos with short video annotations.

DPO for Video-VLMs. As a post-training strategy, DPO has been frequently adopted in the development of VLMs [15, 28, 49, 61, 63]. Unlike the next-token prediction used in the SFT step, DPO refines the VLM using triplets of queries, preferred and rejected responses, reducing model hallucination and better aligning with human reasoning [16]. The simplicity and strong performance of DPO training further encourage researchers to apply it to video-based VLMs, where devising effective spatial and temporal perturbation tasks is a crucial part [2, 16, 18, 23, 24, 55, 58]. Recent works propose methods such as frame cutout, spatial misalignment, clip dropping, clip rearrangement, and frame disconnection to generate query and corresponding preference responses, with the help of proprietary or open-sourced models [16, 23, 24, 55]. The curated data are employed in the VLM training to enhance the spatial-temporal perception and dynamic modeling capabilities for videos. While these efforts have shown promise, many existing works focus on minute-level or short-form videos. While recent efforts [39, 5, 44] have shown promise in long text context alignment for LLMs, extending this paradigm to video presents distinct challenges. For long visual context, directly generating preference data remains challenging due to task design complexity and high computation costs, a limitation even in recent explorations of DPO for long videos [23, 24]. Therefore, our progressive DPO training that incrementally extends the model's capacity to capture long temporal dependencies may offer a more practical and scalable path towards long video understanding.

Video 2

Anchor Query:

What colors are used in the

gradient background

after the

scene

sunset over mountains

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The gradient background of the

end screen template alternates

between pink and blue hues.

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The gradient background

features soft shades of orange,

yellow, and pink.

Preferred Response:

1/4" Ratchet, 8mm Socket,

Phillips Head Screwdriver.

Dis-preferred Response:

Socket wrench or ratchet,

wire brush, screwdriver, and

8mm socket.

Natural Long Video

Stage Ⅱ

: Adaption to Real-World Long-Video Scenarios

Evidence:

Scene #1

mentions a 1/4' ratchet, 8mm socket.

Scene #2

further adds T-30 Torx bit, and 10mm

socket and Phillips Head Screwdriver.

Query:

What tools are listed as required for

installing the cold air intake system?

temporal

mis-align

Mixed Interleaved Short Clips

diversify

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Video N

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Dynamic Visual Cues Binding

diversify

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short captions

Scene#1,

Scene#2,

Scene#3, ...

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Long-Text Context Transfer

业

THANKS FOR WATCHINC

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3 Method

3.1 Background

Direct Preference Optimization (DPO) [36] aligns a policy model π θ with human preferences by directly optimizing a policy that best satisfies the preferences, using a simple classification loss. The objective function is formulated as maximizing the log-sigmoid of the log-likelihood ratio between preferred ( y + i ) and dispreferred ( y -i ) responses, relative to a frozen reference model π ref :

where σ denotes the sigmoid function, and β > 0 is a hyperparameter that controls the strength of the preference margin, effectively determining how sharply the policy should prefer y + i over y -i .

3.2 Distribution Shift from Short to Long Video Contexts

Consequently, directly applying a model fine-tuned on short-video data to preference optimization tasks in long videos reveals two critical challenges:

Reference-model degradation in extended contexts: As depicted in Fig. 1, the shortcontext visual model not only exhibits position bias but also shows over-specialization to short temporal spans, limiting its generalization to extended video contexts. Therefore, in the DPO framework, the reference model π ref is typically kept frozen. As the policy model

π θ is trained to understand and leverage long-range dependencies inherent in extended video sequences, its output characteristics for queries over these long videos may diverge substantially from the short-video patterns that π ref was trained to evaluate. This divergence can impair the ability of π ref to serve as a consistent and meaningful baseline in the DPO objective, potentially leading to unstable or suboptimal policy updates when adapting to long-video tasks.

Annotation scarcity and distribution gap: The lack of dense, high-quality annotations for long video sequences creates a significant distributional disparity between the data available for initial supervised fine-tuning (SFT) or reference model training (typically short clips) and the target domain of long videos. This data gap can severely degrade model performance when generalizing to longer contexts.

3.3 Stage 1: Efficient Short-to-Long Learning from Anchored Cues

To mitigate the distribution shift from short to long video contexts and effectively leverage abundant short-video SFT data without compromising performance on short clips, we design an anchor-based approach that preserves short-context fidelity while exposing the model to long-range contextual variation. This strategy encompasses three core components: the synthesis of anchor-centric preference triples, their subsequent refinement through filtering, and a specific adaptation of the DPO objective to robustly incorporate this data.

The synthesis of preference data forms the initial component. At its core is the generation of preference triples ( q i , y + i , y -i ) where the query q i is answerable only from a designated "anchor" short clip within a longer composite video x i . This Dynamic Visual Cues Binding process involves three key steps:

Anchor-centric QA and Preferred Response Generation: A short video clip, potentially with supplementary annotations (e.g., captions), is randomly selected from SFT data to serve as the anchor x i, anchor . A question-answer (QA) pair ( q i , y + i ) is then generated by the target short VLM such that q i can only be answered comprehensively using information present exclusively within x i, anchor . The answer y + i constitutes the preferred response.

Composite Sequence Assembly: Multiple distinct short clips, including the designated anchor x i, anchor , are concatenated to form the longer composite video sequence x i = [ x i, 1 , ..., x i, anchor , . . . , x i,k ] .

Plausible Dispreferred Response Generation: For the same question q i , a dispreferred response y -i is generated. This response is designed to be plausible yet incorrect by drawing information from non-anchor clips within x i to simulate anchor positioning errors.

Following data synthesis, a critical step is to ensure the quality and unambiguity of the preference signals. To guarantee that the anchor clip x i, anchor is genuinely unique in containing the necessary information to answer q i , we introduce two complementary post-filtering mechanisms:

Scene-Similarity Filtering: Any non-anchor clips within x i whose vision embedding (e.g., DINOv2 [33]) similarity to the anchor x i, anchor exceeds a predefined threshold are replaced, or the entire sample is discarded. This ensures greater visual dissimilarity between the anchor and distractor segments.

Question Specificity Filtering: An Optional LLM (e.g., Qwen-2.5 32B [50]) verifies that q i depends on multiple distinct visual elements within x i, anchor . Questions lacking this specificity, which could be answered by other clips, are discarded.

As discussed previously, applying the short-clip reference model π ref to the full input x i leads to performance degradation due to context-length mismatch. To address this, we introduce an anchoronly approximation , which leverages the design hypothesis that only the anchor clip x i, anchor contains information necessary to answer q i , while non-anchor segments provide no relevant signal. This hypothesis is supported by our filtering process, which reduces semantic similarity between anchor and non-anchor clips, reinforcing the anchor's informational sufficiency.

Under this approximation, the reference model's likelihood is evaluated solely on the anchor clip:

This avoids context-length mismatch, reduces computational and memory costs, and ensures likelihoods reflect only anchor-related content. The modified DPO objective thus becomes:

3.4 Stage 2: Self-Training for Long Video Preference Alignment

While the efficient Short-to-Long Learning method scales input length via synthetic clip compositions, such sequences often lack the natural coherence and narrative structure of real long videos, which is crucial for preference learning that depends on temporally grounded reasoning and causal event understanding. This becomes problematic for queries requiring temporal reasoning of multiple cues (e.g., action chains or evolving events). To bridge this gap, we propose a self-training framework for aligning with long-video preferences.

Data Preparation. In this stage, we first employ a recursive captioning strategy to generate dense textual descriptions for long videos. For each temporally segmented scene within a long video, the target model is conditioned on both the current video segment and the captions generated for preceding scenes. This iterative process constructs a coherent, context-aware caption sequence for the entire video, capturing local semantics and their broader contextual dependencies.

Construction of Preference Data ( q i , y + i , y -i ) for Self-Training. The generation of preference triples involves a multi-step process, leveraging an LLM for query generation and the target MLLM for response generation.

Long-Context Knowledge Transfer. We prompt an LLM to process the video content (represented by scene IDs and captions) to produce a pair ( q i , r i ) . Here, q i is a video-related query, and r i is a detailed reasoning trace. Crucially, r i must explicitly cite specific scene IDs (e.g., 'Scene #N'), which serve as binary scene-question relevance labels .

Preferred Response ( y + i ) Generation. For each query q i and the corresponding full long video x i , the target MLLM π θ is prompted to generate a response. This output, denoted as π θ ( y | q i , x i ) , serves as the preferred response y + i . This step utilizes the model's capability to reason over the full visual context to articulate a comprehensive answer.

Dispreferred Response ( y -i ) Generation. Dispreferred responses y -i are generated by degrading the visual context provided to the target MLLM. We employ two strategies to induce plausible but flawed responses: Reasoning from Partial Evidence , where the MLLM is prompted with only a subset of the relevant scenes identified in r i to force incomplete reasoning; and Hallucination from Irrelevance , where the model is prompted using only non-relevant scenes (those not cited in r i ). The resulting outputs, which typically hallucinate details or miss critical evidence, serve as the dispreferred responses y -i .

For Stage 2, we employ the standard DPO objective L stage 2 ( θ ) = L DPO ( θ ) . While the self-generated y + i may not be perfect, the relative preference delta ( y + i , y -i ) provides a valid training signal. The policy model π θ is initialized from the Stage 1 checkpoint, and the reference model π ref is frozen as the Stage 1 checkpoint, which has been equipped with the basic capability to retrieve query-relevant clips from the full long-video input.

3.5 Total Objective

In both stages i = 1 , 2 , we incorporate the SFT loss into the DPO framework, weighted by α following [34]. The total objective is defined as:

where L stage i ( θ ) denotes the DPO loss at stage i , π θ ( y + | x ) represents the model likelihood of the preferred response y + .

4 Experiment

4.1 Implementation Details

Baseline. We adopt InternVL-2.5-8B [8] as the base model of our framework. It comprises InternViT300M as the vision encoder and InternLM-2.5-7B-32K [4] as the language backbone. According to the official report, the model was trained on a maximum of approximately 32 video frames, corresponding to a visual context length of around 8192 tokens. We implement DeepSpeed Ulysses sequence parallelism to enable efficient training with 32K extended video context length.

Data Preparation. To ensure a fair and leakage-free evaluation, we rely solely on publicly available datasets. Specifically, Stage 1 training utilizes caption annotations from LLaV A-Video-178K [59]. For Stage 2, we incorporate scene-segmented but unlabeled long videos from Vript [51], both of which are included in the InternVL-2.5 SFT dataset. In total, we utilize 16k training samples, consisting of 10k for Stage 1 and 6k for Stage 2.

For Stage 1 data, we preprocess each source clip from LLaVA-Video-178K by uniformly sampling up to 64 frames at 1 fps. To construct each composite training instance x i , we designate one clip as the anchor video ( x i, anchor ) and randomly select several additional clips as non-anchor video . Any non-anchor video whose DINO embedding cosine similarity with the anchor exceeds 0.6 is discarded.

For Stage 2 data, leveraging the pre-segmented scenes in Vript, we employ an external LLM (Qwen2.5-32B) to generate complex queries requiring cross-scene reasoning based on scene captions, along with a relevance chain identifying scenes associated with the question. The preferred response

is generated by feeding the model the query and only the critical video scenes determined by the chain. Conversely, the dispreferred response is synthesized by intentionally inducing errors based on the strategies detailed in Sec. 3.4.

Video Understanding Benchmarks. To comprehensively evaluate our model's long-context understanding capabilities, we adopt three existing long-video benchmarks [45, 48, 62] along with the comprehensive benchmark VideoMME [14]. Additionally, we verify performance on MVBench [20]. The evaluated video durations from all these benchmarks span from a few seconds to 2 hours.

4.2 Main Results

Our main results are shown in Tab. 1. LongVPO exhibits strong competitiveness among models of comparable scale on long-context video understanding benchmarks. Notably, most competing long-video models are trained on datasets with manual curation [12, 37] or rely on proprietary MLLMs [32, 38] to annotate. In contrast, our approach leverages only around 16K synthetic samples, without any reliance on expensive human annotations or closed-source tools, underscoring the effectiveness of our training strategy and data construction methodology.

LongVPO maintains strong performance in short-video analysis. Although our primary goal is to improve long-video understanding, our model also achieves competitive results on the general-purpose short-video benchmark MVBench [20], even surpassing prior results with a +1.1 improvement. This further illustrates the effectiveness of LongVPO in accommodating videos of varying durations in real-world scenarios.

Effectiveness of stage 1. In Stage 1, the model is trained on synthetic long-video data, resembling a form of structureless memory training. The primary goal is to mitigate context bias and activate the model's localization ability. As shown in Tab. 1, the model generalizes well to real-world scenarios and the objectives are largely fulfilled.

Effectiveness of stage 2. Instead of Stage 1 focusing on localizing a single segment to answer questions, Stage 2 focuses on aggregating information across multiple segments from real long videos, thereby enhancing the model's capacity to extract complex, question-relevant content. We observe consistent improvements in most settings, indicating that training on real video domains results in better alignment with realistic scenarios.

Long video context evaluation. Following the NIAH setup from LongVA [57], we densely sample multiple frames from a long video and insert a selected image at different positions within the sampled frames. We evaluate InternVL2.5 on a maximum of approximately 3k frames. The results in Fig. 4 show that the baseline model begins to exhibit significant performance degradation at around 800 frames, with complete failure to follow instruction output formats when reaching approximately 1k frames, while LongVPO demonstrates superior long-context modeling capabilities.

4.3 Extending to Dedicated Long-Context Models

While the primary results are based on short-video models, we further validate the generalizability of our approach by applying it to InternVideo2.5 [46], a representative long-video model. InternVideo2.5 incorporates two key designs for long-context understanding: (1) pre-training on high-quality longvideo datasets, supporting up to 256 frames during training; (2) a specialized redundancy compression mechanism in its vision-language connector for efficient long-context processing.

Generality beyond short-context models. Although initially designed under short-video assumptions, our method demonstrates strong generalization. When applied to InternVideo2.5 for continued training, LongVPO consistently surpasses its counterparts trained on InternVL2.5. This indicates that InternVideo2.5 has not yet saturated in long-context understanding, and our approach provides further enhancement, underscoring its adaptability even for models pre-trained on long videos.

Context length scalability in Stage1. We evaluate the scalability of our approach under varying context lengths. For Stage 1, we extend the length of the synthesized video by simply selecting more clips. Our approach consistently improves performance as the maximum number of input frames increases (e.g., from LongVPO-256f to LongVPO-512f), demonstrating strong scalability and an enhanced ability to exploit longer temporal contexts.

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4.4 Ablation Study

4.4.1 Data Preparation

Scene filtering in stage 1. As shown in Tab. 2, we conduct ablations on scene filtering and response selection. In Stage 1, removing the scene filter-particularly when relying on simple Top-K selectionresults in performance degradation. This underscores the importance of semantic filtering for robust long video understanding.

Chosen response in stage 2. In Stage 2, we compare three approaches for generating the chosen response: the target VLM directly processing the original video to produce responses (self-generated), responses generated by Qwen2.5-32B as used in our long-text context transfer method (Qwen-32B selected), and the target VLM receiving a combination of video frames and synthetic captions as input (interleaved). Surprisingly, the latter two methods yield suboptimal performance, highlighting the efficiency of our training process with only scene-question relevance labels.

LLM backbone in stage 2. LongVPO maintains strong performance even when using the 7B parameter InternLM2.5 as its backbone instead of Qwen2.5-32B, with only a slight drop observed. This demonstrates that its effectiveness is not dependent on a larger LLM.

4.4.2 Baseline Comparison

SFT w/ Caption

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(Training: 7.1h)

Ours

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Scaling with input frames. As the number of input frames increases, LongVPO exhibits progressively larger performance gains, whereas the baseline model (InternVL-2.5-8B) shows signs of saturation. As shown in Fig. 6, LongVPO achieves superior performance across benchmarks, in contrast to the baseline, which stagnates with longer contexts. These results confirm that LongVPO can more effectively extract and utilize temporal information from extended video sequences, highlighting its advantage in long-video understanding.

Stage 1 training strategy. As illustrated in Fig. 5, fine-tuning with single-video caption data leads to a noticeable performance drop. We attribute this to overfitting, since these captions were already part of the SFT dataset. Notably, conducting SFT on our synthesized Stage 1 data reverses this trend and achieves performance gains, validating the efficacy of our data construction pipeline. Furthermore,

applying DPO using these preference pairs yields the most significant improvements, suggesting that the DPO framework enables more data-efficient learning and mitigates overfitting. Building on these findings to optimize DPO for long videos, we observe that, consistent with Fig. 1, using complete long videos as input to the reference model leads to suboptimal outcomes. Our ablation study confirms that this approach is less effective than our proposed method on both general and long-video benchmarks, while our method requires only approximately 70% of its training time.

4.4.3 Qualitative Comparison

Fig. 7 evaluates long video understanding through a pumpkin-carving action counting task. This challenging benchmark requires both action recognition and temporal instance tracking across extended durations. Current strong multimodal models (Qwen2.5-VL, Qwen2-VL, LLaVA-Video) failed to provide correct counts, while our LongVPO accurately identified all 5 instances. This demonstrates LongVPO's superior temporal understanding and counting capability in long videos, outperforming otherwise powerful baselines in complex comprehension tasks.

In this video, how many instances are there of the 'carving pumpkin' action scene in total?

There are 5 instances of the 'carving pumpkin' action scene in total.

𝑼𝒔𝒆𝒓

𝑳𝒐𝒏𝒈𝑽𝑷𝑶

𝑰n𝒕𝒆𝒓𝒏𝑽𝑳𝟐. 𝟓

There are 2 instances of the 'carving pumpkin' action scene in the video.

Time

𝑸𝒘𝒆𝒏𝟐.𝟓 - 𝑽𝑳

There are 21 instances of the 'carving pumpkin' action scene in total.

𝑸𝒘𝒆𝒏𝟐 - 𝑽𝑳

The answer is 1.

𝑳𝑳𝒂𝑽𝑨 - 𝑽𝒊𝒅𝒆𝒐

There are two instances of the 'carving pumpkin' action scene in total.

5 Conclusion

We have proposed LongVPO, a novel two-stage DPO training framework tailored for long video understanding. By leveraging synthetic DPO instances constructed from short visual contexts, our method incrementally extends the capabilities of short-context VLMs to long-context comprehension, without relying on any annotated long-video data. Compared with specialized long-video models, LongVPO achieves the state-of-the-art on both long and short video understanding benchmarks. These advances highlight its potential as a general-purpose solution for long video understanding.

Limitations. Our work prioritizes performance improvement over inference computational efficiency. We will explore the integration with existing context compression approaches in future research.

References

Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 , 2023.

Daechul Ahn, Yura Choi, San Kim, Youngjae Yu, Dongyeop Kang, and Jonghyun Choi. Isr-dpo: Aligning large multimodal models for videos by iterative self-retrospective dpo. arXiv preprint arXiv:2406.11280v2 , 2025.

Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou, and Weizhu Chen. Make your llm fully utilize the context. Advances in Neural Information Processing Systems , 37:62160-62188, 2024.

Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, Keyu Chen, Xin Chen, Xun Chen, Zehui Chen, Zhi Chen, Pei Chu, et al. Internlm2 technical report. arXiv preprint arXiv:2403.17297 , 2024.

Guanzheng Chen, Xin Li, Michael Qizhe Shieh, and Lidong Bing. Longpo: Long context self-evolution of large language models through short-to-long preference optimization. In International Conference on Learning Representations , pages 6638-6654, 2025.

Lin Chen, Xilin Wei, Jinsong Li, Xiaoyi Dong, Pan Zhang, Yuhang Zang, Zehui Chen, Haodong Duan, Bin Lin, Zhenyu Tang, Li Yuan, Yu Qiao, Dahua Lin, Feng Zhao, and Jiaqi Wang. Sharegpt4video: Improving video understanding and generation with better captions. arXiv preprint arXiv:2406.04325 , 2024.

Yukang Chen, Fuzhao Xue, Dacheng Li, Qinghao Hu, Ligeng Zhu, Xiuyu Li, Yunhao Fang, Haotian Tang, Shang Yang, Zhijian Liu, Yihui He, Hongxu Yin, Pavlo Molchanov, Jan Kautz, Linxi Fan, Yuke Zhu, Yao Lu, and Song Han. LongVILA: Scaling long-context visual language models for long videos. In The Thirteenth International Conference on Learning Representations , 2025.

Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, et al. Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling. arXiv preprint arXiv:2412.05271 , 2024.

Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al. Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 24185-24198, 2024.

Chuanqi Cheng, Jian Guan, Wei Wu, and Rui Yan. Scaling video-language models to 10k frames via hierarchical differential distillation. arXiv preprint arXiv:2504.02438 , 2025.

Zesen Cheng, Sicong Leng, Hang Zhang, Yifei Xin, Xin Li, Guanzheng Chen, Yongxin Zhu, Wenqi Zhang, Ziyang Luo, Deli Zhao, et al. Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms. arXiv preprint arXiv:2406.07476 , 2024.

Miquel Farré, Andi Marafioti, Lewis Tunstall, Leandro Von Werra, and Thomas Wolf. Finevideo. https: //huggingface.co/datasets/HuggingFaceFV/finevideo , 2024.

Jiajun Fei, Dian Li, Zhidong Deng, Zekun Wang, Gang Liu, and Hui Wang. Video-ccam: Enhancing video-language understanding with causal cross-attention masks for short and long videos, 2024.

Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li, Shuhuai Ren, Renrui Zhang, Zihan Wang, Chenyu Zhou, Yunhang Shen, Mengdan Zhang, Peixian Chen, Yanwei Li, Shaohui Lin, Sirui Zhao, Ke Li, Tong Xu, Xiawu Zheng, Enhong Chen, Caifeng Shan, Ran He, and Xing Sun. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 24108-24118, 2025.

Jinlan Fu, Shenzhen Huangfu, Hao Fei, Xiaoyu Shen, Bryan Hooi, Xipeng Qiu, and See-Kiong Ng. Chip: Cross-modal hierarchical direct preference optimization for multimodal llms. arXiv preprint arXiv:2501.16629 , 2025.

Haojian Huang, Haodong Chen, Shengqiong Wu, Meng Luo, Jinlan Fu, Xinya Du, Hanwang Zhang, and Hao Fei. Vistadpo: Video hierarchical spatial-temporal direct preference optimization for large video models. arXiv preprint arXiv:2504.13122 , 2025.

Zhenpeng Huang, Xinhao Li, Jiaqi Li, Jing Wang, Xiangyu Zeng, Cheng Liang, Tao Wu, Xi Chen, Liang Li, and Limin Wang. Online video understanding: Ovbench and videochat-online. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 3328-3338, 2025.

Yogesh Kulkarni and Pooyan Fazli. Videopasta: 7k preference pairs that matter for video-llm alignment. arXiv preprint arXiv:2504.14096 , 2025.

Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Peiyuan Zhang, Yanwei Li, Ziwei Liu, and Chunyuan Li. Llava-onevision: Easy visual task transfer, 2024.

Kunchang Li, Yali Wang, Yinan He, Yizhuo Li, Yi Wang, Yi Liu, Zun Wang, Jilan Xu, Guo Chen, Ping Luo, Limin Wang, and Yu Qiao. MVBench: A comprehensive multi-modal video understanding benchmark. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 22195-22206, 2024.

Kunchang Li, Yinan He, Yi Wang, Yizhuo Li, Wenhai Wang, Ping Luo, Yali Wang, Limin Wang, and Yu Qiao. VideoChat: Chat-centric video understanding. Sci. China Inf. Sci. , 68, 2025.

Pengyi Li, Irina Abdullaeva, Alexander Gambashidze, Andrey Kuznetsov, and Ivan Oseledets. Maxinfo: A training-free key-frame selection method using maximum volume for enhanced video understanding. arXiv preprint arXiv:2502.03183 , 2025.

Rui Li, Xiaohan Wang, Yuhui Zhang, Zeyu Wang, and Serena Yeung-Levy. Temporal preference optimization for long-form video understanding. arXiv preprint arXiv:2501.13919 , 2025.

Shicheng Li, Lei Li, Kun Ouyang, Shuhuai Ren, Yuanxin Liu, Yuanxing Zhang, Fuzheng Zhang, Lingpeng Kong, Qi Liu, and Xu Sun. Temple:temporal preference learning of video llms via difficulty scheduling and pre-sft alignment. arXiv preprint arXiv:2503.16929 , 2025.

Xinhao Li, Yi Wang, Jiashuo Yu, Xiangyu Zeng, Yuhan Zhu, Haian Huang, Jianfei Gao, Kunchang Li, Yinan He, Chenting Wang, et al. VideoChat-Flash: Hierarchical compression for long-context video modeling. arXiv preprint arXiv:2501.00574 , 2024.

Bin Lin, Yang Ye, Bin Zhu, Jiaxi Cui, Munan Ning, Peng Jin, and Li Yuan. Video-llava: Learning united visual representation by alignment before projection. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing , 2023.

Jiajun Liu, Yibing Wang, Hanghang Ma, Xiaoping Wu, Xiaoqi Ma, Xiaoming Wei, Jianbin Jiao, Enhua Wu, and Jie Hu. Kangaroo: A powerful video-language model supporting long-context video input, 2024.

Ziyu Liu, Yuhang Zang, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Haodong Duan, Conghui He, Yuanjun Xiong, Dahua Lin, and Jiaqi Wang. Mia-dpo: Multi-image augmented direct preference optimization for large vision-language models. arXiv preprint arXiv:2410.17637 , 2024.

Zuyan Liu, Yuhao Dong, Ziwei Liu, Winston Hu, Jiwen Lu, and Yongming Rao. Oryx MLLM: On-demand spatial-temporal understanding at arbitrary resolution. In The Thirteenth International Conference on Learning Representations , 2025.

Zhijian Liu, Ligeng Zhu, Baifeng Shi, Zhuoyang Zhang, Yuming Lou, Shang Yang, Haocheng Xi, Shiyi Cao, Yuxian Gu, Dacheng Li, Xiuyu Li, Haotian Tang, Yunhao Fang, Yukang Chen, Cheng-Yu Hsieh, De-An Huang, An-Chieh Cheng, Jinyi Hu, Sifei Liu, Ranjay Krishna, Pavlo Molchanov, Jan Kautz, Hongxu Yin, Song Han, and Yao Lu. Nvila: Efficient frontier visual language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 4122-4134, 2025.

Muhammad Maaz, Hanoona Rasheed, Salman Khan, and Fahad Shahbaz Khan. Video-chatgpt: Towards detailed video understanding via large vision and language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics , 2024.

OpenAI. Hello gpt-4o. In OpenAI Blog , 2024.

Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski. DINOv2: Learning robust visual features without supervision. Transactions on Machine Learning Research , 2024. Featured Certification.

Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, He He, Sainbayar Sukhbaatar, and Jason Weston. Iterative reasoning preference optimization. In Advances in Neural Information Processing Systems , pages 116617-116637. Curran Associates, Inc., 2024.

Bowen Peng, Jeffrey Quesnelle, Honglu Fan, and Enrico Shippole. YaRN: Efficient context window extension of large language models. In The Twelfth International Conference on Learning Representations , 2024.

Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems , 36:53728-53741, 2023.

Ruchit Rawal, Khalid Saifullah, Miquel Farré, Ronen Basri, David Jacobs, Gowthami Somepalli, and Tom Goldstein. Cinepile: A long video question answering dataset and benchmark. arXiv preprint arXiv:2405.08813 , 2024.

Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv preprint arXiv:2403.05530 , 2024.

Weizhou Shen, Ziyi Yang, Chenliang Li, Zhiyuan Lu, Miao Peng, Huashan Sun, Yingcheng Shi, Shengyi Liao, Shaopeng Lai, Bo Zhang, Dayiheng Liu, Fei Huang, Jingren Zhou, and Ming Yan. Qwenlong-l1.5: Post-training recipe for long-context reasoning and memory management. arXiv preprint arXiv:2512.12967 , 2025.

Xiaoqian Shen, Yunyang Xiong, Changsheng Zhao, Lemeng Wu, Jun Chen, Chenchen Zhu, Zechun Liu, Fanyi Xiao, Balakrishnan Varadarajan, Florian Bordes, Zhuang Liu, Hu Xu, Hyunwoo J. Kim, Bilge Soran, Raghuraman Krishnamoorthi, Mohamed Elhoseiny, and Vikas Chandra. LongVU: Spatiotemporal adaptive compression for long video-language understanding. In Forty-second International Conference on Machine Learning , 2025.

Yunhang Shen, Chaoyou Fu, Shaoqi Dong, Xiong Wang, Yi-Fan Zhang, Peixian Chen, Mengdan Zhang, Haoyu Cao, Ke Li, Xiawu Zheng, Yan Zhang, Yiyi Zhou, Ran He, Caifeng Shan, Rongrong Ji, and Xing Sun. Long-vita: Scaling large multi-modal models to 1 million tokens with leading short-context accuracy. arXiv preprint arXiv:2502.05177 , 2025.

Yan Shu, Zheng Liu, Peitian Zhang, Minghao Qin, Junjie Zhou, Zhengyang Liang, Tiejun Huang, and Bo Zhao. Video-xl: Extra-long vision language model for hour-scale video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 26160-26169, 2025.

Enxin Song, Wenhao Chai, Guanhong Wang, Yucheng Zhang, Haoyang Zhou, Feiyang Wu, Haozhe Chi, Xun Guo, Tian Ye, Yanting Zhang, et al. Moviechat: From dense token to sparse memory for long video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 18221-18232, 2024.

Zecheng Tang, Zechen Sun, Juntao Li, Qiaoming Zhu, and Min Zhang. Logo-long context alignment via efficient preference optimization. arXiv preprint arXiv:2410.18533 , 2024.

Weihan Wang, Zehai He, Wenyi Hong, Yean Cheng, Xiaohan Zhang, Ji Qi, Xiaotao Gu, Shiyu Huang, Bin Xu, Yuxiao Dong, et al. Lvbench: An extreme long video understanding benchmark. arXiv preprint arXiv:2406.08035 , 2024.

Yi Wang, Xinhao Li, Ziang Yan, Yinan He, Jiashuo Yu, Xiangyu Zeng, Chenting Wang, Changlian Ma, Haian Huang, Jianfei Gao, et al. Internvideo2. 5: Empowering video mllms with long and rich context modeling. arXiv preprint arXiv:2501.12386 , 2025.

Hongchen Wei, Zhihong Tan, Yaosi Hu, Chang Wen Chen, and Zhenzhong Chen. Longcaptioning: Unlocking the power of long video caption generation in large multimodal models. arXiv preprint arXiv:2502.15393 , 2025.

Haoning Wu, Dongxu Li, Bei Chen, and Junnan Li. Longvideobench: A benchmark for long-context interleaved video-language understanding. In The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track , 2024.

Yuxi Xie, Guanzhen Li, Xiao Xu, and Min-Yen Kan. V-dpo: Mitigating hallucination in large vision language models via vision-guided direct preference optimization. arXiv preprint arXiv:2411.02712 , 2024.

An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115 , 2024.

Dongjie Yang, Suyuan Huang, Chengqiang Lu, Xiaodong Han, Haoxin Zhang, Yan Gao, Yao Hu, and Hai Zhao. Vript: A video is worth thousands of words. Advances in Neural Information Processing Systems , 37:57240-57261, 2024.

Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, et al. Minicpm-v: A gpt-4v level mllm on your phone. arXiv preprint arXiv:2408.01800 , 2024.

Jiabo Ye, Haiyang Xu, Haowei Liu, Anwen Hu, Ming Yan, Qi Qian, Ji Zhang, Fei Huang, and Jingren Zhou. mPLUG-owl3: Towards long image-sequence understanding in multi-modal large language models. In The Thirteenth International Conference on Learning Representations , 2025.

Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence , pages 9127-9134, 2019.

Liping Yuan, Jiawei Wang, Haomiao Sun, Yuchen Zhang, and Yuan Lin. Tarsier2: Advancing large vision-language models from detailed video description to comprehensive video understanding. arXiv preprint arXiv:2501.07888 , 2025.

Hang Zhang, Xin Li, and Lidong Bing. Video-LLaMA: An instruction-tuned audio-visual language model for video understanding. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations , 2023.

Peiyuan Zhang, Kaichen Zhang, Bo Li, Guangtao Zeng, Jingkang Yang, Yuanhan Zhang, Ziyue Wang, Haoran Tan, Chunyuan Li, and Ziwei Liu. Long context transfer from language to vision. arXiv preprint arXiv:2406.16852 , 2024.

Ruohong Zhang, Liangke Gui, Zhiqing Sun, Yihao Feng, Keyang Xu, Yuanhan Zhang, Di Fu, Chunyuan Li, Alexander Hauptmann, Yonatan Bisk, et al. Direct preference optimization of video large multimodal models from language model reward. arXiv preprint arXiv:2404.01258 , 2024.

Shaolei Zhang, Qingkai Fang, Zhe Yang, and Yang Feng. LLaVA-mini: Efficient image and video large multimodal models with one vision token. In The Thirteenth International Conference on Learning Representations , 2025.

Yuanhan Zhang, Jinming Wu, Wei Li, Bo Li, Zejun Ma, Ziwei Liu, and Chunyuan Li. Video instruction tuning with synthetic data. arXiv preprint arXiv:2410.02713 , 2024.

Zhiyuan Zhao, Bin Wang, Linke Ouyang, Xiaoyi Dong, Jiaqi Wang, and Conghui He. Beyond hallucinations: Enhancing lvlms through hallucination-aware direct preference optimization. arXiv preprint arXiv:2311.16839 , 2023.

Junjie Zhou, Yan Shu, Bo Zhao, Boya Wu, Zhengyang Liang, Shitao Xiao, Minghao Qin, Xi Yang, Yongping Xiong, Bo Zhang, Tiejun Huang, and Zheng Liu. Mlvu: Benchmarking multi-task long video understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 13691-13701, 2025.

Yiyang Zhou, Chenhang Cui, Rafael Rafailov, Chelsea Finn, and Huaxiu Yao. Aligning modalities in vision large language models via preference fine-tuning. arXiv preprint arXiv:2402.11411 , 2024.

Appendix Overview

This appendix provides additional details of our approach and further experimental results, organized as follows:

Section A describes the core experimental design behind our context-bias analysis.

Section B showcases qualitative results across various scenarios.

Section C outlines implementation details of our method.

A Core Experimental Design for Context Position Bias Probing (Main Fig. 1)

Evaluation Setup. We directly selected tasks from MVBench [20] for evaluation. Only unambiguous tasks were included to ensure the validity of the labels.

To evaluate models designed primarily for short-context input, we introduce a simplified evaluation framework: (1) Padding Strategy: We simulate long-context scenarios by embedding the original video frame sequence into a larger grid (akin to high-resolution image tiling), surrounded by meaningless padding frames. (2) Random Placement: The original frames are randomly placed within this padded grid to test whether a model's performance is sensitive to the spatial location of meaningful content relative to the padding.

This experiment aims to validate two key constraints for an ideal long-video context model: (1) Consistency across Context Lengths: A well-designed model should maintain consistent performance across both short and long contexts without altering the task semantics. (2) Position Invariance: Since padding frames carry no meaningful information, the spatial location of valid video frames within the padded grid should not affect task performance.

As shown in Main Fig. 1, existing long-context models fall short of these expectations: (a) Shifted Long-Context Consistency: Performance varies with the distance between query and relevant frames, showing an undesirable sensitivity to position. An ideal long-video model should attend equally to relevant frames regardless of their location in the input. (b) Short-Long Context Discrepancy: Compared to our LongVPO, existing models exhibit a significant performance drop when transitioning from short to long context inputs. This suggests unreliable long-video understanding. When used directly as the reference model in DPO-style fine-tuning with long-context inputs, these models may lead to suboptimal performance. In contrast, LongVPO maintains nearly identical performance across short and long contexts, validating its robust design.

B Qualitative Results

We present additional qualitative comparisons with state-of-the-art models on long-video tasks across diverse scenarios. Despite being trained on synthetic data, our model demonstrates competitive open-ended QA performance, maintaining robustness across various domains.

Ideal Long Context

Video-Language Model

Same Query

shift

Ideal Long Context

Video-Language Model

Query:

What direction is the cyan sphere

moving in within the video?

Ideal Long Context

Video-Language Model

Same Query

Expected Same Answer

(b) Short-Long Context Consistency

Expected Same Answer

(a) Shifted-Long Context Consistency

Blank Frame

Frame/Token

Shifted L1 Distance

Video Frame

padding

Ours

InternVL2.5-8B

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rerrormance

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Shifted Distance

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State Change

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ZT

What color is the computer used by the man during the meeting?

The computer used by the man during the meeting is silver.

𝑼𝒔𝒆𝒓

𝑳𝒐𝒏𝒈𝑽𝑷𝑶

𝑰n𝒕𝒆𝒓𝒏𝑽𝑳𝟐. 𝟓

The computer used by the man during the meeting is black.

Time

𝑸𝒘𝒆𝒏𝟐.𝟓 - 𝑽𝑳

The color of the computer used by the man during the meeting is not specified in the given

information.

𝑸𝒘𝒆𝒏𝟐 - 𝑽𝑳

The color of the computer used by the man during the meeting is not specified in the given video.

𝑳𝑳𝒂𝑽𝑨 - 𝑽𝒊𝒅𝒆𝒐

The computer is black.

。

What is the purpose of the two signing a contract?

The purpose of the two signing a contract is to formalize the agreement for the blood transfusion.

𝑼𝒔𝒆𝒓

𝑳𝒐𝒏𝒈𝑽𝑷𝑶

𝑰n𝒕𝒆𝒓𝒏𝑽𝑳𝟐. 𝟓

The two are signing a contract to buy a yacht.

Time

𝑸𝒘𝒆𝒏𝟐.𝟓 - 𝑽𝑳

The purpose of the two signing a contract is to establish a business agreement or partnership.

𝑸𝒘𝒆𝒏𝟐 - 𝑽𝑳

The purpose of the two signing a contract is to formalize their agreement and ensure that both parties

are on the same page.

𝑳𝑳𝒂𝑽𝑨 - 𝑽𝒊𝒅𝒆𝒐

The purpose of the two signing a contract is to transfer ownership of the land.

At the beginning of the video, how do the person in light-colored clothes and the person in dark-

colored clothes enter the scene?

The person in light-colored clothes and the person in dark-colored clothes enter the scene by climbing

over a chain-link fence.

𝑼𝒔𝒆𝒓

𝑳𝒐𝒏𝒈𝑽𝑷𝑶

𝑰n𝒕𝒆𝒓𝒏𝑽𝑳𝟐. 𝟓

They run into the scene.

Time

𝑸𝒘𝒆𝒏𝟐.𝟓 - 𝑽𝑳

The person in light-colored clothes and the person in dark-colored clothes enter the scene at the same

time.

𝑸𝒘𝒆𝒏𝟐 - 𝑽𝑳

The person in light-colored clothes and the person in dark-colored clothes enter the scene by walking

towards the camera.

𝑳𝑳𝒂𝑽𝑨 - 𝑽𝒊𝒅𝒆𝒐

The person in light-colored clothes runs towards the camera, while the person in dark-colored clothes

enters from the right.

What falls when the woman is eavesdropping?

The recorder.

𝑼𝒔𝒆𝒓

𝑳𝒐𝒏𝒈𝑽𝑷𝑶

𝑰n𝒕𝒆𝒓𝒏𝑽𝑳𝟐. 𝟓

A phone.

Time

𝑸𝒘𝒆𝒏𝟐.𝟓 - 𝑽𝑳

The video clip does not provide enough information to determine what falls when the woman is

eavesdropping.

𝑸𝒘𝒆𝒏𝟐 - 𝑽𝑳

The woman is eavesdropping on a conversation about a game.

𝑳𝑳𝒂𝑽𝑨 - 𝑽𝒊𝒅𝒆𝒐

A red balloon falls.

C Implementation Details

Hardware Setup. All experiments were conducted on a server equipped with 4 × 8 NVIDIA H100 GPUs, each with 80GB of memory. We implement DeepSpeed Ulysses sequence parallelism to enable memory-efficient training.

Training Strategy. We adopt the proposed LongVPO method for training, which involves a two-stage fine-tuning process on a curated dataset of 16k samples-10k samples for stage 1 and 6k for stage 2. Each model variant is trained for 1 epoch, balancing training efficiency with the need for robust adaptation.

Full-Model Fine-Tuning. We fine-tune the entire model end-to-end. This includes the vision encoder, the vision-language connector, and the LLM backbone.

Optimization Settings. We use a composite loss that balances KL-divergence and supervised finetuning (SFT) objectives, with both weights set to 1.0 ( β =0.01, α =1.0). The learning rate is set to 5e-7, with batch size 8, a cosine learning rate scheduler, and a warm-up ratio of 0.01 to stabilize early training dynamics.

Training Duration. Each model variant requires approximately 10 hours of training with DeepSpeed Ulysses sequence parallelism enabled; otherwise, training completes in about 1 hour under the aforementioned configuration.

Evaluation Settings. For consistency and comparability, we follow the evaluation protocols established by InternVL2.5 and InternVideo2.5. The maximum number of frames per input is set to 512 to test the model's scalability in long-context understanding.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: In the abstract and introduction sections, we have outlined the contributions of our work and provided a summary at the end of the introduction.

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The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

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It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: In the conclusion section, we discuss the limitations of our work and suggest directions for future improvements.

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The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

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The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

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Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA]

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Peer reviews:
comments: SUMMARY:
The paper tackles long-form video understanding without relying on expensive long-video annotations. It introduces LongVPO, a two-stage Direct Preference Optimization (DPO) pipeline. Using 16k synthetic preference pairs and no human labels, LongVPO outperforms previous open-source models on four long-video benchmarks.

STRENGTHS AND WEAKNESSES:
S1: Achieves strong long-video gains with only 16 k synthetic triples and no new human labels.

S2: Stage 1 addresses position bias; Stage 2 adds multi-segment reasoning, mirroring long-text LLM alignment.

S3: Introduces anchor-centric synthetic triples and applies both visual-similarity and question-specific filtering to keep supervision unique. The anchor-only approximation lets the reference model score just the anchor clip, avoiding long-sequence degradation while cutting compute.

W1: 	Competing long-video models are evaluated with their released weights; none are fine-tuned on the authors’ 16k dataset, so part of the gain may stem from extra training rather than technique.

W2: More ablations needed including fine-tune InternVL-2.5-8B with your Stage 1 data without DPO to isolate DPO’s contribution

W3: In Stage 2, “preferred” answers are self-generated and then reinforced via DPO, risking self-reinforcement bias. Consider adding independent judges (human or a separate VLM) or at least reporting results against ground-truth QA sets.

QUESTIONS:
In Tab. 1 you compare LongVPO‐Stage 1/2 with other long-video VLMs that are not re-trained on your 16K synthetic triples. Could you  fine-tune the public InternVL-2.5-8B baseline on exactly your Stage 1 dataset under the same compute budget without any of your Stage 1/2 data

LIMITATIONS:
Please see weakness
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-01

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper propose LongVPO, a two-stage Direct Preference Optimization framework that enables short-context vision-language models to understand ultra-long videos without long-video annotations. In Stage 1, it generates synthetic preference data using short clips and filtered distractors to train the model efficiently. In Stage 2, it creates reasoning tasks from real long videos using recursive captioning and large language models for self-supervised fine-tuning. With only 16K synthetic examples and no human labels, LongVPO achieves state-of-the-art performance on multiple long-video benchmarks while retaining strong short-video understanding.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper is well-written with clear and coherent expression.\n2. Half of the paper's motivation—how to efficiently generate high-quality training prompts for long videos—is highly meaningful.\n3. The data generation pipeline in the two-stage DPO framework appears promising and may serve as a potential solution to the scarcity of training corpora for long-video tasks.\n\nWeaknesses:\n1. The other half of the paper's motivation—how to extend short-context VLMs to long videos—seems questionable. Unlike a year ago, most current foundation VLMs (e.g., Qwen2.5-VL, Video-LLaMA3, Seed1.5-VL) already support long context lengths and typically process videos using fixed FPS inputs. On this basis, extending short-context VLMs is no longer a necessary direction, either now or in the foreseeable future.\n2. On certain datasets, increasing the number of input frames leads to very limited improvements, or even significant performance drops. This undermines the overall motivation of the paper and should be discussed in detail. Moreover, constructing a weaker baseline and showing improvements over it carries limited value. The reported relative gains of LongVPO should be adjusted to discount this inflated baseline effect.\n\nQUESTIONS:\n1. Please seriously address the concerns raised in the Weaknesses section regarding the motivation and results. As a suggestion, validating the effectiveness of LongVPO on models with longer context VLMs (e.g., Qwen2.5-VL) may help alleviate these concerns.\n2. DPO often causes the policy model to significantly deviate from the reference model. How does LongVPO address this issue, and are there any strategies employed to mitigate such deviations?\n\nLIMITATIONS:\nPlease refer to the Strengths And Weaknesses", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a fine-tuning method derived from direct preference optimization (DPO) for long-form video understanding. The authors observed two limitations of existing video language models. With a simple padding, the authors empirically show that the video-language models have positional bias in Figure 1. In addition, in Figure 4, the authors analyzed the performance degradation of existing video-language models by evaluating performance across various lengths of videos. As the video gets longer, the baseline models show significant performance loss. The authors tackled the problem without sacrificing the short-video performance via a two-stage post-training method. First stage utilizes anchored cues and modified DPO loss with them. And in the second stage, self-training was applied. Overall, the proposed method provides considerable performance gain on multiple video benchmarks, including LVBench, LongVideoBench, MLVU,  Video-MME, and MVBench.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths.** \n\n1. **Presentation.** This paper is well-organized and reads well. This paper started from an interesting observation that the video models have positional bias. Also, fine-tuning for long-form videos leads to degradation in short-form video performance. The motivations/observation in Figure 1. ad Figure 4.and the overall pipeline in Figure 2 are helpful to understand the main argument.\n2. **Motivation.** This a bit sounds redundant but the presentation was good but also the observations themselves are interesting. In many studies, people think that the longer video understanding is challenging and improving longer video understanding is the only focus. But improving long and short video understanding both is quite interesting.\n3. **Interesting trick.** Anchor-only approximation and the DPO objective with the anchor is a simple and effective trick.Presumably, this may accelerate fine-tuning since the inference of reference model gets faster. Also, the reference model (probably a existing model for short-form videos at the beginning) will provide a better reference probability.\n\n**Weaknesses.**\n\n1. **Analysis for approximation error.** Although the approximation with anchored cues is an interesting technique, the approximation gap is not fully analyzed. The experiment to check whether the approximation provides a performance boost or degradation. \n2. **Incomplete comparison.** The main table, table 1 has many missing values. Especially, InternVL2.5 with 64 frames looks strong. Also, it is comparable or more competitive than InternVL2.5 with 512 frames. Unfortunately, some entries for InternVL2.5 with 64 frames are missing which outperforms on average compared to InternVL2.5 with 512 frames. If the table has all entries, then the effectiveness of the proposed method can be evaluated more accurately.\n3. **No computational complexity comparison.**  The proposed method is a two-stage approach. It requires new tricks. Although the proposed method provides the performance boost, without the comparison regarding computational cost, it is hard to precisely evaluate the value of the proposed method.\n\nQUESTIONS:\n1. Why better robustness is important to understanding long-form videos? The figure 1 explains one of the main observations that the baseline methods exhibit a significant degradation when the location of the main content changes. It is not clear why tackling this problem improves long video understanding. \n\n2. Did the authors observe any artifacts or disadvantages of fine-tuning with synthetic or artificially derived data?\n\nLIMITATIONS:\nLimitation of this work was discussed by the authors with two lines.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces LongVPO, a new two-stage training framework designed to enhance the long-video understanding capabilities of Vision-Language Models (VLMs) without relying on costly human annotations for long videos. The core problem it addresses is that existing VLMs, typically trained on short clips, struggle with ultra-long videos due to a lack of relevant training data and inherent positional biases (a \"lost-in-the-middle\" effect). The authors demonstrate that LongVPO significantly outperforms existing open-source models on several long-video benchmarks (LVBench, LongVideoBench, MLVU, Video-MME) and maintains strong performance on short-video tasks (MVBench), all while using only 16K synthetic examples and no direct human labels for long videos.\n\nSTRENGTHS AND WEAKNESSES:\nThis paper presents an interesting and promising approach to the challenging problem of long-form video understanding. The proposed two-stage framework sounds interesting, and the empirical results show notable gains over existing open-source models.\n\nStrengths\n- The paper tackles a timely and highly relevant problem. Developing scalable methods for long-video understanding that do not rely on prohibitive annotation costs is a significant goal for the community.\n- The proposed two-stage DPO framework seems interesting. The \"anchored cues\" concept in Stage 1, especially the pragmatic approximation of the reference model to handle long contexts, is a clever way to adapt DPO to this setting. The self-training loop in Stage 2 is an attempt at enabling models to generate their own complex training curricula.\n- The model demonstrates strong performance, achieving state-of-the-art results on multiple long-video benchmarks compared to other open-source models. This suggests the proposed training strategy is effective.\n\n\nWeaknesses / Major Concerns\n- My primary concern is the heavy reliance on a very powerful external model (Qwen-2.5 32B) for the critical data generation step in Stage 2. It is difficult to disentangle the contribution of the LongVPO framework itself from the sheer capability of this external LLM to generate high-quality reasoning tasks. The paper presents itself as a self-contained method, but its success may be largely attributable to an external, unanalyzed component. This makes it hard to assess the generalizability and true effectiveness of the framework.\n- The paper argues that approximating the DPO reference model in Stage 1 is a key contribution. However, it fails to include a direct comparison against a standard DPO baseline where the reference model processes the full synthetic video. Without this experiment, the claim that the approximation is necessary for performance (and not just efficiency) is unsubstantiated. This is a significant gap, as it leaves a core methodological claim partially unverified.\n\nQUESTIONS:\nA critical piece of evidence is missing from the ablation study. To convincingly argue for the necessity of the reference model approximation in Stage 1 (a central claim), it seems essential to compare it against the most direct baseline: standard DPO training where the reference model is fed the entire interleaved clip sequence. \n\nCould you please provide this comparison? If the standard DPO baseline performs significantly worse, it would strongly validate your proposed approximation as a critical and necessary innovation. Without this, the motivation for the approach remains incomplete.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-05"}