LongVPO: From Anchored Cues to Self-Reasoning for Long-Form Video Preference Optimization
Authors: Zhenpeng Huang
Jiaqi Li
Zihan Jia
Xinhao Li
Desen Meng
Lingxue Song
Xi Chen
Liang Li
Limin Wang
Venue: NeurIPS 2025 poster ()
Keywords: Video Large Language Models; Long Video Understanding; Preference Optimization
Primary area: applications
TL;DR: 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.

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
Zhenpeng Huang!.
Jiaqi Li Zihan Jia'
Xinhao Li Desen Meng'
Lingxue Song² Xi Chen Liang Li
Limin Wang" 133,1
State Key Laboratory for Novel Software Technology, Nanjing University
JJUTTIN Research Shanghai AI Laboratory
https: http://ttub..om////tU///////////
Abstract
We present LongVPO, a novel two-stage Direct Preference Optimization frame- work 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 individual short clips, interleaving them with distractors, and applying visual-similarity and question-specifictty fil- tering 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, employ a recur- sive captioning pipeline on long videos to generate scene-level metadata, then use large language model to craft multi-segment reasoning queries and dispreferred responses, aligning the model's 's preferences through mulli-segment reasoning tasks. With only 16K synthetic examples and no costly human labels, LongVPO outper- forms the mate-of-the-arr open-source models multiple long-video benchmarks, while maintaining strong short-video performance (e.g., on MVBench), offering a scalable paradigm for efficient long-form video understanding.
Introduction
have demonstrated impressive capa- bilities both image and video understanding. However, their performance often degrades when applied to tasks that require long-context visual reasoning. such analyzing videos that span over This presents a significant challenge in scaling VLMs for long-form video
Recent vision-language models (VLMs) 32 60
25
an hour [48] 14 62)
understanding.
While recent progress in long-video VLMs 43 40
has been encouraging, most approaches rely heavily costly, high-quality annotations for long vidcos, limiting their scalability in practical applications. In contrast, existing short-context dess-deespte being trained only on limited-frame inputs-hard have shown surprisingly competitive results 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 video modeling if properly extended. This raises natural question: How far can we push short-contexi VLMs into the long-video regime-without the burden of expensive re-training 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:
Equal contribution.
Corresponding author (Imwang @ njj.edu.cn).
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure Context Position Bias Probing. Left: A short video segment (visualized as a 4 x grid) is embedded within much longer padded sequence and processed chronologically. Right: Performance is plotted against each frame's L.1 distance from the question token. The middle-position drop indicates a strong positional bias "Jost-in-the-midlle" The Upper Bound shows performance without padding, revealing degradation under long-context settings.
High-quality video-text annotations, such detailed captions question ansserr pairs, 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 [4]] (2) Context-Leength
Bias in Short-Context VLMs: Common practices such as YARN (55, NTK extend positional
encodings for longer sequences, but the resulting models still suffer from position-relater biases and limited performance gains. To simulate long-context scenarios, we embed the original short video's ID frame sequence within a much longer sequence, padding the surrounding positions with blank frames (visualized spatial positions across extended contexts. Specifically, by computing the L1 distance between each bost-in-the-middee
grid in Fig. D. This setup allows to investigate the model's sensitivity to
frame's position and fixed query point, we uncover 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. IT.
phenomenon analogouu
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 two-stage training framework that extends short-context VLMs to ultra-long video contexts, as shown in Fig. 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- Solf-Traning for Long Video Preference Alignment. Building on Stage I's memory and retrieval single-segment skills, we train the model to handle longer, more complex videos, without requiring ground-trus -ruth annotations. First, we employ a recursive captioning pipeline to generate structured, scene-level metadata to leverage the model's coort-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-Preferres Responses by prompting the model with partial or misleading context (e.g., omitting critical scenes), encouraging to assemble the complete information chain required for accurate answers in real-world, long-video scenarios.

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In summary, our contributions are threefold:
We introduce LongVPO, a two-stage framework that extends short-context VLMs 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 con- text for long vidcos, using only - 16k cestances-significann fewer than existing instruction- tuning 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
VLMs for Long Video Understanding. Advancements in rision-large models (VLMs) [8] 21 20 26 31 60] have shown impressive capabilities video understanding. and many models demonstrated excellent performance in short video analysis. Some recent works make fur- ther efforts towards long video understanding by design- ing certain strategies to compress/select visual context 10
Previous Methods
Quary
Policy Mudil
Shart-Caatest Viduo Languege Model
Rederence Model 10
(shor: context)
Ours- Stage
42 or extend the temporal context 41 57.. addition to the innovation model architecture, it is also important to construct long-form video instruction datasets to guide VLMs in extracting detailed visual cues and modeling coos-temporal rela- tionships. Representative methods such as VideoChat- Flash (3), 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 strat- egy employ short video-text data facilitate VLMs in long vidco understanding remains a challenge. In this work, we propose novel two-stage framework for VLMs progressively Iearn the ability to analyze longer videos with short video annotations.
Interleaved Short Clips Query
Policy Modil
13
17
22 40
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Shart- Contest
Langaage
Reference Model (
Clip Quary
(shorscontrxd)
Stage
Video Query
Policy Model
Long-C Vilue Langsoge Modal
Relivess Model 18
(loggeomeett)
Figure 2: Comparison of prior methods
with our proposed two-stage method.
DPO for Video- As a post-training strategy, DPO has been frequently adopted in the development of VLMs [5 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 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 proprictary or open-sourced models []6] 23 24 (55). The curated data are employed in the VLM training to enhance the spatial-temports perception and dynamic modeling capabilities for videos. While these efforts have shown promise, many existing works focus on minute-level or fhort-form videos. While recent efforts [39 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 more practical and scalable path towards long video understanding.
3

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Stage Efficient Long-Context Modeling via Interleaved Short Clips
Mixed Interleaved Short Clips
Dynamic Visual Cues Binding
diversify
divaniity
Video 2
Video
Vidco N
lemporal mis-alian
Short- Contt-Contet Visual Language Model
Preferred Response:
Dis-preferred Response:
Query:
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gradient background
colors are used
features soft shades orange.
gradient backgroun scene sunset over mountains?
between pink and blue hues.
yellow, and pink.
Stage II Adaption Real-World Long- Video Scenarios
Natural Long Video
Synthetic
short captions
Query:
Scench1,
What tools are listed as required for
SeencH2.
installing the cold intako system?
Sceneli,...
Long-Context Visual Language Model
Long-Tex Text Context Transfer
Preferred Response:
Dis-preferred Response:
Evidence:
Ratcher, Smm Socket, 10mm Socket, T:30 Torx Bit, Phillips Head Screwdriver.
Socket wrench or ratehet, wire brush, screwdriver, and 8mm socket.
Scone #1 mentions a 1/4" ratchet, 8mm socket. Scone further T-30 and 10mm socket and Phillips Head Screwdriver.
Figure 3: Overview of our two-stage training framework. Stage 1: form pseudo-long video. The target model generates the query (g:) and preferred response (y conditioned on anchor clip and its caption, while dispreferred responses (v/ are generated by prompting the model to answer based on non-anchor (distractor) clips, simulating temporal misalignment errors. Stage For unlabeled long videos, an L.M generates the query (gi) and reasoning trace (r:) based scene synthetic captions. The target MLLM then generates the preferred response (y," " based on corresponding scene context, and generates dispreferred responses (y; under degraded context (c.g., partial or irrelevant scenes).
Short clips are concatenated to
Method
3.1 Background
Direct Preference Optimization (DPO) (36] aligns policy model me with human preferences by directly optimizing policy that best satisfies the preferences, using simple classification loss. The objective function formulated as maximizing the log-sigmoid of the log-likelihood ratio between preferred (v;t) and dispreferred (v, responses, relative to a frozen reference model Wref
(D)o( = Σ log
log
To (v,
log
No (v.
x2))
)
(1)
Teat (1,
xx.)
Toof (v
xx:)
where a denotes the sigmoid function, and 3 > 0 is hyperparameter that controls the strength of the preference margin, effectively determining how sharply the policy should prefer yft over y;
3.2 Distribution Shift from Short to Long Video Contexts
Consequently, directly applying model fine-tuned on short-video data to preference optimization tasks in long videos reveals two critical challenges:
Referencc-model degradation in extended contexts: As depicted in Fig. 1 the short- context 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 Nye typically kept frozen. As the policy model

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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 Tyef was trained to evaluate. This divergence can impair the ability of Tref to serve as 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 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 short clips, we design anchor-based approach that preserves short-context fidelity while exposing the model to long-range contextual vari- ation. 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 (g:, V, y4 where the query qi is answerable only from designated "anchor" short clip within a longer composite video z, 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 (c.g., captions), is randomly selected from SFT data to serve as the anchor aicanrh A question-answer (QA) pair (g;, y,*) is then generated by the target short VLM such that qi can only be answered comprehensively using information present exclusively within ainnchrr- The answer constitutes the preferred response.
Composite Sequence Assembly: Multiple distinct short clips, including the designated anchor Li,anchor are concatenated to form the longer composite video sequence z; [Ti, "i., , J...].
Plausible Dispreferred Response Generation: For the same question qi, a dispreferred response yi is generated. This response is designed to be plausible yet incorrect by drawing information from non-anchor clips within x; 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 aiaanchrr is genuinely unique in containing the necessary information to answer qi, we introduce two complementary post-filtering mechanisms:
Any non-anchor clips within x; whose vision embedding (e.g., DINOv2 (33) similarity to the anchor aincco exceeds predefined threshold replaced, or the entire sample is discarded. This ensures greater visual dissimilarity between the anchor and distractor segments.
Scene-Similarity Filtering:
Question Specificity Filtering: An Optional LLM (e.g., Qwen-2.5 32B (50)) verifies that qi depends on multiple distinct visual elements within Ti,anchor- Questions lacking this specificity, which could be answered by other clips, are discarded.
As discussed previously, applying the short-clip reference model कeef to the full input æ; leads to performance degradation due to context-length mismatch. To address this, introduce an anchor- only approximation, which leverages the design hypothesis that only the anchor clip auchorr contains information necessary answer q. 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 's likelihood evaluated solely on the anchor clip:
Tee x.) Re Trer(y I auchor)
(2)
5

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This avoids context-length mismatch, reduces computational and memory costs, and ensures likeli- hoods reflect only anchor-related content. The modified DPO objective thus becomes:
Lstagel (0) = Σ log a
3 log
To(y
log
TT (v;
Iz)
]
(3)
Tret (3)
Di,anchor)
Tree (1)
Ti,hnchor)
3.4
Stage 2: Self-Trainin for Long Video Preference Alignment
While the efficient Short-to-Long Learning method scales imput 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 evolving events). To bridge this gap, we propose 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-awart caption sequence for the entire video, capturing local semantics and their broader contextual dependencies.
Construction of Preference Data (g:, Vy; for Self-Training. The generation of preference triples involves multi-step process, leveraging an LLM for query generation and the target MILM for response generation.
1. Long-Context Knowledge Transfer. We prompt an LLM to process the video content (represented by scene IDs and captions) to produce pair rs) Here, is video-related query, and r; is detailed reasoning trace. Crucially, r; must explicitly cite specific scene IDDs (e.g., "Scene #N"), which serve as binary scene-question relevance labels.
2. Preferred Response (y, Generation. For each query and the corresponding full long video 2i, the target MLLM To is prompted to generate response. This output, denoted as T qs 24), serves as the preferred response y," This step utilizes the model's 's capability to reason over the full visual context to articulate comprehensive answer.
3. Dispreferred Response (y; Generation. Dispreferred responses yi 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 subset of the relevant scenes identified in to force incomplete reasoning; and Hallucination from Irrelevance, where the model is prompted using only non-relevant scenes (those not cited in r:). The resulting outputs, which typically hallucinate details or miss critical evidence, serve as the dispreferred responses y/
For Stage 2, we employ the standard DPO objective Catage, (0) LDPO While the ceff-generated y may not be perfect, the relative preference delta (v," y; provides a valid training signal. The policy model is initialized from the Stage checkpoint, and the reference model Tref is frozen as the Stage I 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 2,2, we incorporate the SFT loss into the DPO framework, weighted by following 34
The total objective is defined as:
(())= = Lstage, (0) + a log mo (x)
(4)
|yt|
where Latage, (0) denotes the DPO loss at stage i, mo(y+] |x) preferred response yt.
represents the model likelihood of the

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BongVideoBench
(wo/w
MVBench
Models
Frannes
Overall 4101s
Validation 473s
M-Avg 651s
Overall 1,10ss
Long 2,386s
Overall 16s
Average Duration
Proprictury Vsion-Langugee Modely
GPPT4-V
Undisclourd
599/62.3
52.5756.
GPT4-o
32
Undisclosed
30.5
66.7
64.5
71,977.2
65.3/72.1
6/166
Gemini- P.5-Po
X
Undiacloued
35.1
64,0
75,0/113
67.4/7744
60.5
Open-Source Mmlge-mage Lingon-L
LLaVA-OneVision
72B
32
61.3
66.4
66.3 09.6
00.07
59.4
IntemVL2
76B
16
61.0
69.9
61.2/67.8
68.6
LLaVA-OneVision
7B
56.5
64.7
58.2/
56.7
Oryx-1.5
TB
128
56.3
67.5
58.8 64.2
MiniCPM-422
88
64
54,9
$7.3
60.9 63.7
$18//6.3
iPL.UG-Dwi1
7B
16
52.1
63.7
59.3
50.1
Qwan2-VL.
50
2FPS
NVILA
7R
256
70.1
Open-Source Langungn Models
VadeoLLaMMA
72B
61.2
62/4661.7
/7.6/59.0
Vidco-X1. 47
7B
2,0488
49.5
64.9
55.5/61.0
49.2/
Videal.L.MM
56
7B
16
48.5
45.9
Viden-CCAM
9B
53.2/57.4
46.7/49.9
Kanganoo
27
8R
54.8
61.0
56.0155
46.7/
61.0
LongVUl
40
78
1FP8
65.4
60.6 1 1.59.5
66.9
LongVA
67
7B
128
56.3
/ 54.3
46.2 47.6
LongVII
VideoChat-Flssh
7B
512
64.7
65.1/69.7
55.4633.
latemVL2.5 g
8B
64
43.2
60.0
68.9
61.2//66 66.9
72.0
Intem/VL2.5*
8B
512
45.2
62.7
67.6
61.1 /65.3
/5.1/
72.0
+LongVPO
Stage1
88
512
49.4 (+17)
65.4 (477)
23.5 (+19))
64.2
(111) 70.1 (-1.)
53.8
62.8 (+5m)
72.9 (w09))
Stage2
8B
512
50.1 (+19)
66.6 (-9
74.1
64.6
(-10) / 70.3 (4.0)
55.3
(-1 64.2 (+700)
73.1
+Long Stape1
8B
512
49.6 (44.0)
66.0 (5..))
74.8 (7.2)
65.0
(11.; 971.2 (55))
55.8
7/ 65.1 (17.)
72.9
Insern Video2.5* 5 [6
SB
512
47.4
63.2
72.8
63.3/71.1
$2.6/ 65.1
75.7
+Long
Stape1
SB
512
50.9 (+1.5)
67.0 (-10))
74.0 (+1.7)
65. (1..)) (-1.)
54.3 mi (+1.))
74.7
Stage2-iterl
8B
512
51.0 (3:0)
67.2 (144))
74.4 (1..0)
(/.66
(2.55:144) (:14)
(4.9)123)))6 66.1 (120
75.1
Stape2-Hte2
8B
512
51.0 (3.0))
67.2 (+4.0)
74.7
66.1
(-2.8) /73.
56,1
(63.5) 67,4
75.1
VPo (5120)
Stagel
SB
512
50.4 (13.0)
67.8 (14.6)
75.0 (12.))
65.6
1.3)
/ (.13)
$4.9 (12.5)
67.1 (1200)
75.1
Table 1: Accuracy (%) on the short and long video understanding benchmarks. Size indicates the number of parameters. Frames denotes the maximum number of frames sampled from each video or the frame sampling rate (FPS). The best and second-best results among open-soure models of similar size (7~9B) are in bold and underlined, respectively. "256f"/"512f refer to the maximum number of training frames. * denotes reproduced results.
4 Experiment
4.1
Implementation Details
Baseline. We adopt Lntem 8 as the base model of our framework. comprises Intern ViT- 300M as the vision encoder and IntemLLM-2.5-77-3322 [ as the language backbone. According to the official report, the model was trained on a maximum of approximately 32 video frames, corresponding to visual context length of around 8192 tokens. We implement DeepSpeed Ulysses sequence parallelism 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 ---VV-o------- Stage 2, we incorporate scene-segmented but unlabeled long videos from Vript [51]. both of which are included in the InternVL-2. SFT dataset. In total, we utilize 16k training samples, consisting of 10k for Stage and 6k for Stage 2.
[59]. For
For Stage data, we preprocess each source clip from VLLVeo- 178K by uniformly sampling up to 64 frames at fps. To construct each composite training instance I;, we designate one clip as the anchor video (2 (Zi,nnchrr) 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 relevance chain identifying scenes associated with the question. The preferred response
7

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is generated by feeding the model the query and only the critical video scenes determined by the chain. Conversely, the dispreferred response synthesized by intentionally inducing errors based on the strategies detailed in Sec. 3.4
Video Understanding Benchmarks. To comprehensively evaluate model's 's long-context under-
standing 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 hours.
4.2
Main Results
Our main results are shown in Tab. []] LongVPO exhibits strong competitiveness among models of comparable scale on long-contens video understanding benchmarks. Notably, most competing 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.
long-video models are trained on datasets with manual curation 12 37
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 ceneral-purpose short-video benchmark MVBench [20), even surpassing prior results with a +1.1 improvement. This VPO
further illustrates the effectiveness of Lon in accommodating videos of varying durations in real-world scenarios.
Effectiveness of stage 1. In Stage 1, the model 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 's localization ability. As shown in Tab. [ the model generalizes well to real-world scenarios and the objectives largely fulfilled.
Effectiveness of stage 2. Instead of Stage focusing on localizing a single segment to answer questions, Stage 2 focuses aggregating information across multiple segments from real long videos, thereby enhancing the model's 's capacity to extract complex, question-relee relevvnn content. We observe consistent improvements in most settings, indicating that training real video domains results in better alignment with realistic scenarios.
Long video context evaluation. Following the NIAH setup from Long [77. we densely sample multiple frames from long video and insert selected image at different positions within the sampled frames. We evaluate Intern on maximum of approximately 3k frames. The results in Fig. 4 show that the baseline model begins to exhibit significant performance degradation around 800 frames, with complete failure to follow instruction output formats when reaching approximately 1k frames, while LongVEs VPO demonstrates superior long-contexx modeling capabilities.
4.3 Extending to Dedicated Long-Context Models
While the primary results are based short-video models, further validate the generalizability of our approach by applying it to InternVideo2.1 Video2.5 [46), a representative long-video model. Intern Video2.5 incorporates two key designs for long-come long-context understanding: (1) pre-training on high-quality long- video 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 viieo assump- tions, method demonstrates strong generalization. When applied to Video... for continued training, VPO consistently surpasses its counterparts trained on InterVVL2.3 This indicates that Intern Video2.5 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 Stagel. We evaluate the scalability of our approach under varying context lengths. For Stage I, 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 Long VPO-256f to Long VPO-512f), demonstrating strong scalability and an enhanced ability to exploit longer temporal contexts.

---

1.0
98
Depth Porcart
60.0
0.4
60.0
044
80.0
022
80.0
100.0
2.48
2.86
0.0
2.06
2.c
00
(a) Baseline Model with 0% acc in around Ik Frames.
(b) Long VPO (Ours)
Figure The V-NIAH results of our baseline Lntem and LongVPO. "Frame Depth" indicates the position where the needle image is located, ranging from 0% to 100% (from the beginning the end of the video).
4.4
Ablation Study
4.4.1 Data Preparation
Scene filtering in stage 1. As shown in Tab. 2 conduct ablations scene filtering and response selection. In Stage 1, removing the scene ciller-prttcularrly when relying on simple Top-K selection- results 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 used in long-text context transfer method (Qwen-32B selected), and the target VI.M receiving a combination of video frames and synthetic captions as input (interleaved). Surprisingly, the latter methods yield suboptimal performance, highlighting the efficiency of our training process with only scene-question relevance labels.
LLM backbone in stage 2. Lon 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
65
64.4
60.5
Performant aurformance
60
$4.6
51A
$0.9
48
43.5
45
AAT
17 Castion 317 Singet DatA
(Trinin:7..1 7.19)
(Tehiig: 4.9%
40
32K
64K
128K
Visual Input Length
Figure 5: Comparison of Stage training using SFT and DPO, Additional results are provided in the appendix.
Figure 6: Performance scaling of Long and VI2ern with respect increas- ing input frame counts.
Scaling with input frames. As the number of input frames increases, LongVPO exhibits progres- sively larger performance gains, whereas the baseline model (Intern VL-2.5-8B) shows signs of saturation. As shown in Fig. 6 LongVPO achieves superior performance across benchmarks, in con- trast to the baseline, which stagnates with longer contexts. These results confirm that Long VPO can more effectively extract and utilize temporal information from extended video sequences, highlighting its advantage in long-vides understanding.
Stage 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 data reverses this trend and achieves performance gains, validating the efficacy of our data construction pipeline. Furthermore.

---

Table 2: Ablation study on scene filtering and response selection methods across all long-viin benchmarks. MLVU, Long LongendeBen,,, and LVBench use a 32K context window during inference.
Stage
Setting
MLVU
LongVideoBench
LVBench
w/ Scene Filter
72.9
66.1
45,3
Stage
w/o Soene Filter (adding similar one)
69.8
64.2
43.4
Scene Filter (TopK)
69.9
58.4
Chosen response Choice
Self-generated response LLM-generated (Qwen2.5-32B) response
72.9
66.1
45,3
Stage 2
73.1
65.6
44.4
Self-generated w/ scene-intee iccne-interleaeed caption
73.0
66.1
44.7
Long Context Knowledge Transfer Rackhone Inleml-M2..57B5 M2.5-7B instead Qwen2.5-321
72.5
65.8
44.9
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. using complete long videos 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. evaluatees 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-VI, Qwen2-VI., 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.
thts cidro, asyy instances
the purpicto! action total?
JiteraF22.5
There are 2 instances of the "cazving purpkinn action scane in the video.
Instances 'camding purpiden
Qam2
LcPt4 Pideo
There are tuo instances of the "caeving pumpite! action scene total.
'carving puppkts'
Figure 7: Qualitative comparison long video understanding. More details are in the appendix.
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, 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 tfateoof-the-ane on both long and short video understanding benchmarks. These advances highlight its potential as a general-purposs 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.
Acknowledgement
This work is supported by the National Key R&D Program of China (No. 2022ZD0160900 the Basic Research Program of Jiangsu (No. BK20250009), Nanjing University-Chiie Mobile Communications Group Co., Ltd. Joint Institute (No. NI20250033), and the Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Appendix Overview
This appendix provides additional details of our approach and further experimental results, organized follows:
Section describes the core experimental design behind our context-bial analysis.
Section
showcases qualitative results across various scenarios.
Section
outlines implementation details of our method.
Model
LVBench
Long Bong
MLVU
Video-MME
Qwen2.5-VL
45.3
56.0
70.2
65.1 /71.6
Intern VL2.5
45.2
62.7
67.6
61.1 / 65.3
LongVPO
50.1 14.9
66.6 13.9
74.1 16.5
64.6 / 70.3 13.5/15.0
Intern Video..
47.4
63.2
72.8
63.3 / 71.1
+ -ongVVO
51.0 13.6
67.2 14.0
74.7 1199
66.1 73.1 12.8/12.0
Video-LLaMA3
45.3
59.8
73.0
66.2 / 70.3
LongVPO
49.8 14.5
63.4 13.6
74.6 11.6
67.2 71.4 1..0/
Table 3: Performance comparison on long video benchmarks. Improvements over base models are shown in red with 1 symbols.
A Core Experimental Design for Context Position Bias Prohing (Main Fig.
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 larger grid (akin to high-resolution image tiling), surrounded by meaning- less padding frames. (2) Random Placement: The original frames are randomly placed within this padded grid to test whether a model's '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) Con- sistency 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. [ existing long-con contectntext 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 position. An ideal long-video model should attend equally to relevant frames regardless of their location in the input. (b) Short-Long Context Diserepancy: Compared
Long VPO, existing models exhibit 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 men-tnnnng with long-context inputs, these models may VPO maintains nearly identical performance across
lead to suboptimal performance. In contrast, LPn 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.
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Frame/Token Sequence Order
Query: What direction sphere moving within video?
Shifted L1 Distance
Blank Frame
Ideal Long Context Video-I Videuo-aanguage Model
Video Frame
T
shift
(a) Shifted-Long Context Consistency
Expected Same Answer
Same Query
Ideal Long Context Video- Mido-Language Model
(b) Short-Long Context Consistency
padding
Expected Same Answer
Same Query
Ideal Long Context Video-Language Model
Figure 8: Core experimental design underpinning the context-bias analysis in Fig.
Upper
Overall Performance
Action Count
Character
90
Moving Direction
Figure 9: Compared the main Fig. 1 we shorten the input video context length (by padding blank
frames to 10 x 10 grid rather than 12 12), yet the same "lost in the middle" phenomenon persists.
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what color the used the during meeting?
fnternPi2.5
The conputer used the san during the meeting black,
@ven2 VL
The color of the computer used by the mann during the neating is not speciFled in the given information.
Qweez VL
The color the computer used by the man during the meeting is not specified in the given video.
The computer is black.
EPo
used by
the meeting is silver.
Figure 10: Long Video Understanding: Visual Semantic Understanding
Time
What is the purpose of the tno signing contract?
IsternFa22
The tno are signing contract to buy yacht. purpose signing
The purpose signing
agreenent partnership.
agreesent
aze same page.
LLa'4 Video
The purpose of signing contract transfor onnership of Land. The purpose of the two signing contract is to fomalize the agreesent for the blood transfusion.
LongXPo
Figure 11: Long Video Understanding: Cross-temporal Scene Association
Time
beginning video,
person cLothes the scene?
person
User
They
The person in light-colored clothes and the person in dark-colored clathes enter the scene the sane
Owenz 5
tine.
Oweez
The person in Light-colored clothes and the person in darle-co clothes enter the scene by walkding
tosards the casera.
Laf' Video
The parson in Light-cotored clothes runs towards the carera, while the person in dark-colored clothes onters from the right.
LPngYPO
The person Light-colored clothes person dazie-colored over chain-1 fence,
scene by climbing
Figure 12: Long Video Understanding: Temporal Order Analysis
17

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Mhat falls when the is cavesdropping?
InternFi2.5
phone.
(uen2
The video clip does not provide enough information to determine what falls when the woran is eavesdropping.
@wee2
The wozan is eavesdropping on conversation about gaze.
Video
VPo
The recorder.
Figure 13: Long Video Understanding: Detail Comprehension
C Implementation Details
Hardware Setup. All experiments were conducted on a server equipped with 4 x 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 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 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 lisiun-language connector, and the L.LM backbone.
Optimization Settings. We use composite loss that balances KL-divergence and supervised fine- tuning (SFT) objectives, with both weights set 1.0 (3=0.01, o=1.0). The learning rate is 5e-7, with batch size 8, cosine learning rate scheduler, and 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 hour under the aforementioned configuration.
Evaluation Settings. For consistency and comparability, follow the evaluation protocols estab- lished by Intern and Intern Video2.5. The maximum number frames per input is set to 512 to test the model's scalability in long-context understanding.
18

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Reviews:
[{"review_id": "ukwdQDWGPY", "reviewer": "Reviewer_FVvM", "summary": "The 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.", "questions": "A 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.", "limitations": "N/A", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "This 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.", "was_revised": false, "final_justification": "", "rebuttal": "**Q1: Concern Regarding the Role of Qwen-2.5 32B in LongVPO Stage2**\n\nWe would like to clarify that LongVPO only uses Qwen-2.5 32B in a limited role: 1) It generates questions from video captions, and 2) provides binary relevance hints for each video segment. Most importantly, Qwen-2.5 32B is not used to generate the model’s preferred responses in Stage 2.\n\nTo further address the reviewer’s concern, we conducted additional ablation studies:\n\n**1. LongVPO without Qwen-2.5 32B exhibits only slight performance degradation.** We replaced Qwen-2.5 32B with InternLM2.5-7B-chat (the backbone LLM of our baseline InternVL2.5-8B). This version, LongVPO-InternLM-Stage2, shows only marginal degradation:\n\n| Model                   | LVBench | LongVideoBench | MLVU | Video-MME |\n| ----------------------- | ------- | -------------- | ---- | --------- | \n| LongVPO-Stage2          | 50.1    | 66.6           | 74.1 | 64.6      |\n| LongVPO-InternLM-Stage2 | 49.7    | 66.2           | 73.7 | 64.4      |\n\n**2. Directly using Qwen2.5-32B Generated Preferred Responses is much worse.** As shown in Table 2 of the main paper: Directly using Qwen-2.5 32B responses as preferred answers for DPO performs worse than using our model’s self-generated responses: with LVBench: -1.7% and LongVideoBench: -1.9% This highlights that the external model does not dominate the learning signal and its deep involvement is not beneficial to performance.\n\n3.The reliance on Qwen-2.5 32B is primarily for its instruction-following and formatting capabilities, e.g., outputting desired JSON-formatted question-answer pairs. **Fundamentally, our method focuses on enabling the VLM to self-correct its own response preferences based on valid guidance, rather than directly depending on a powerful external model—this is also the key distinction from SFT or other methods that rely heavily on strong external supervision.**\n\n**Q2: On the Necessity of Stage 1 Approximation vs. Direct DPO**\n\n* Figure 5 Recap: Our main paper’s Figure 5 shows that standard DPO (reference model on full synthetic video) underperforms LongVPO on Video-MME and LVBench, due to weaker long-context alignment.\n\n* Extended Ablation: We further increased the max video length from the original 128 frames / 32k visual tokens training input to 256 frames / 64k tokens. As training context length grows, LongVPO’s Stage 1 approximation consistently yields extra gains, confirming that our design is not only more efficient but also more effective than a naïve full-video DPO baseline.\n\n**Performance Gains of LongVPO vs. Direct DPO in Stage 1 Data**\n| Max Frames / Visual Context | LVBench Δ | LongVideoBench Δ | Video-MME Δ | Training Cost    |\n| --------------------------- | --------- | ---------------- | ----------- | ---------------- |\n| 128 frames / 32k tokens     | +1.5%     | +0.1%            | +1.2%       | 4.9 h vs. 7.1 h  |\n| 256 frames / 64k tokens     | +2.0%     | +0.9%            | +1.0%       | 8.0 h vs. 11.5 h |\n\n\n\nWe hope these additional results sufficiently address both concerns, as there is only minimal impact of external LLM reliance and the effectiveness of our Stage 1 approximation beyond efficiency."}, {"review_id": "0pNXZu3ssm", "reviewer": "Reviewer_4ZfK", "summary": "This 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.", "questions": "1. 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?", "limitations": "Limitation of this work was discussed by the authors with two lines.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The proposed method achieves a significant performance gain compared to various methods, including one of the most popular alignment method, Direct DPO. Also, it improves efficiency as well. Lastly, the author response addresses the major concerns originally raised.", "rebuttal": "**Q1: Stage 1 Approximation Error Analysis**\n\nTo directly analyze the “approximation gap,” we conducted a head-to-head comparison between Stage 1 LongVPO and a standard DPO baseline (reference model on the full synthetic video).\n\nThe table below summarizes the results, including the original 128-frame/32k-token setting from Figure 5 of the main paper, and an extended 256-frame/64k-token experiment that further demonstrates scalability.\n\n**Performance Gains of LongVPO vs. Direct DPO (Stage 1)**\n| Max Frames / Visual Context | LVBench Δ | LongVideoBench Δ | Video-MME Δ | Training Cost    |\n| --------------------------- | --------- | ---------------- | ----------- | ---------------- |\n| 128 frames / 32k tokens     | +1.5%     | +0.1%            | +1.2%       | 4.9 h vs. 7.1 h (70%)  |\n| 256 frames / 64k tokens     | +2.0%     | +0.9%            | +1.0%       | 8.0 h vs. 11.5 h (70%) |\n\nStage 1 approximation consistently improves performance, especially at longer contexts.\n\n**Q3: Computational Complexity**\n\nFrom the table in Q1:\n* Stage 1: Our LongVPO Stage 1 achieves better performance than standard DPO while requiring ~30% less training time, demonstrating its efficiency advantage.\n* Stage 2: The training cost is identical to standard DPO, ensuring that the overall improvement does not come at additional computational expense. Both cost 4.4h.\n\n**Q2: Incomplete Comparison (Table 1)**\n\n* **For InternVL 64Frames**, we corrected the missing/broken entries in our initial submission and fixed a known LVBench score error (72.0 → 43.2) for InternVL2.5 (64 frames), consistent with the reproduced results in Video-LLaMA3’s report. **The updated results confirm that the InternVL-2.5 (512 frames) model is the strongest baseline.**\n\n| Model Variant     | LVBench          | LongVideoBench | MLVU | Video-MME | MVBench | AVG\\* |\n| ----------------- | ---------------- | -------------- | ---- | --------------- | ------- | ----- |\n| InternVL2.5 (64)  | 43.2 (corrected) | 60.0           | 68.9 | 64.2 / 66.9\t           | 72.0    | 61.9  |\n| InternVL2.5 (512) | 44.7             | 62.7           | 67.6 | \t63.3 / 66.9           | 72.0    | 62.4  |\n| + LongVPO Stage 1 | 46.8             | 66.1           | 72.9 | 64.2 / 70.1           | 72.9    | 65.2  |\n| + LongVPO Stage 2 | 50.1             | 66.6           | 74.1 | 64.6 / 70.3           | 73.1    | 66.3  |\n\n*AVG is computed using the mean of the two Video-MME scores.\n\n* **Regarding the missing data for models other than InternVL**, all figures in the table are cited directly from their respective official reports. These entries are missing because the original authors did not publish results for these specific benchmarks.\n\n**Q4: Why Localizing Robustness Matters in Long-Form Video Understanding？**\n\nRobustly localizing key information and gathering information to answer correctly is not just a proxy task; it is a fundamental prerequisite for any meaningful long-video understanding as a Common solution paradigm [1][2]. The reasoning is twofold:\n* **Sparsity of Information**: User queries about long videos often depend on a few critical moments. The model must first reliably find this \"needle\" before it can reason about it.\n* **Prerequisite for Reasoning**: Complex reasoning requires integrating evidence, often from multiple, non-contiguous scenes. If the model cannot robustly locate each piece of evidence, its reasoning process fails at the first step.\n\nOur positional bias experiment in Figure 1 directly simulates this challenge. This is analogous to the widely used \"needle-in-a-haystack\" tests[3] in long-text evaluation, which are designed specifically to pressure-test a model's ability to retrieve sparse, critical information from a long, noisy context. Without this robustness, a model cannot truly comprehend long-form content.\n\n**Q5: Artifacts of Tuning with Synthetic Data**\n\nWe did observe minor stylistic artifacts in a small subset of open-ended generation tasks (e.g., summarization). These typically involve the model adopting a structural narration style, using phrases like \n> \"The video begins with...\" or \"The scene transitions to...\".\n\nThese artifacts are a byproduct of the captioning style used to generate training data and **do not harm factual accuracy on question-answering tasks (Please see the qualitative comparison in the main paper and appendix for more examples)**. We view this as a data diversity challenge that can be effectively addressed in future work with simple techniques like style-aware prompting, rather than a fundamental flaw in the model's reasoning capabilities.\n\n**References**\n\n[1] Deep Video Discovery: Agentic Search with Tool Use for Long-form Video Understanding\n\n[2] VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos\n\n[3] Needle In A Haystack – Pressure Testing LLMs"}, {"review_id": "afmQeJF06e", "reviewer": "Reviewer_dFSN", "summary": "This 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.", "questions": "1. 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?", "limitations": "Please refer to the Strengths And Weaknesses", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Given the excellent new results presented by the authors, as well as their contributions in long-video data construction and the DPO algorithm, I still think this paper is above the bar.", "rebuttal": "**Q1. On the motivation of extending short-context VLMs**\n\nWe would like to clarify that our goal is not simply to \"extend\" legacy models, but to address a more fundamental and persistent challenge: **how to efficiently align the powerful, pre-existing semantic capabilities of foundation models for robust long-video reasoning.**\n\n**Our central insight is not model-specific but applies broadly: \"Transferring and aligning strong short-context capabilities into long-context reasoning can be more efficient than direct long-context tuning.\"**\n\nWhile it is true that many foundation VLMs (e.g., Qwen2.5-VL, Video-LLaMA3, Seed-VL) now claim to support long-context inputs, several observations challenge the effectiveness of such support:\n\n* The training data landscape exhibits a pronounced long-tail distribution in context length. The vast majority of available multimodal supervision—such as image task pairs or short video clips—consists of short-context data. In stark contrast, annotated long videos suitable for instruction tuning or reasoning tasks are extremely scarce, as they demand substantial computational resources and costly labeling efforts. As a result, existing models risk being insufficiently trained for genuine long-context understanding.\n\n\n* On long-video benchmarks such as LongVideoBench, Video-MME, or LVBench, many long-context VLMs experience performance degradation as input length increases, highlighting the gap between architectural support and actual long-range reasoning ability.\n\n| Model          | LVBench         | LongVideoBench  | MLVU            | Video-MME                     |\n| -------------- | --------------- | --------------- | --------------- | ----------------------------- |\n| Qwen2.5-VL     | 45.3            | 56.0            | 70.2            | 65.1 / 71.6                   |\n| InternVL2.5       | 44.7            | 62.7            | 67.6            | 63.3 / 66.9                   |\n| + LongVPO      | **50.1** (+5.4) | **66.6** (+3.9) | **74.1** (+6.5) | **64.6 / 70.3** (+1.3 / +3.4) |\n| InternVideo2.5 | 46.4            | 60.6            | 72.8            | 63.3 / 71.1                   |\n| + LongVPO      | **52.4** (+6.0) | **67.9** (+7.3) | **74.5** (+1.7)       | **66.1 / 73.0** (+2.8 / +1.9)              |\n| Video-LLaMA3   | 45.3            | 59.8            | 73.0            | 66.2 / 70.3                   |\n| + LongVPO      | **49.8** (+4.5) | **63.4** (+3.6) | **74.6** (+1.6) | **66.7 / 71.4** (+0.5 / +1.3)              |\n\n**LongVPO is a highly efficient and general alignment method for both short- and long-context models.**\n\n- **First**, InternVL2.5-LongVPO itself already achieves performance that surpasses or matches most dedicated long-video models.  \n- **More crucially**, our approach remains highly effective even when applied to specialized video models such as Video-LLaMA3 and InternVideo 2.5, which have already been fine-tuned on valuable long-video data.  \n  - This is particularly noteworthy because the LLaVA-Video-178k dataset, from which we generate our preference data, was already included in the initial supervised fine-tuning (SFT) of these models.  \n  - The fact that LongVPO still delivers significant additional gains demonstrates that its strength comes from the **alignment method itself**, not merely from exposure to new data, highlighting its power as a truly **efficient and general alignment technique**.\n\n  \n**Q2. On limited gains from increasing frame count.**\n\nThere is no significant drop in performance with more frames in our baseline models. **The previously reported LVBench score of 72.0 for InternVL2.5 (64 frames) was a known error; we have corrected it to 43.2, in line with the results reported in Video-LLaMA3's technical report.**\n\nIncreasing the number of input frames from ​​64 → 512​​ yields consistent improvements (+0.6% avg). When combined with our ​​Stage 1​​ and ​​Stage 2​​ training, performance improves substantially, **absolute gain over the baseline: ​​+5.2%​​ (64frames), +4.6% (512frames).**\n| Model Variant     | LVBench  | LongVideoBench | MLVU     | Video-MME       | AVG\\*      |\n| ----------------- | -------- | -------------- | -------- | --------------- | -------- |\n| InternVL2.5 (64)     | 43.2 (corrected)   | 60.0           | 68.9     | 64.2 / 66.9     | 59.4     |\n| InternVL2.5 (512)    | 44.7     | 62.7           | 67.6     | 63.3 / 66.9     | 60.0     |\n| + LongVPO Stage 1         | 46.8     | 66.1           | 72.9     | 64.2 / 70.1     | 63.2     |\n| + LongVPO Stage 2 | **50.1** | **66.6**       | **74.1** | **64.6 / 70.3** | **64.6** |\n\n*The average of two Video-MME scores is used to calculate the global average.  \n  \n**Q3. On DPO deviation from the reference model**\n\nWe adopt a widely-used solution by Joint optimization with SFT loss (i.e.,  $L_{\\text{total}} = L_{\\text{LongVPO}} + L_{\\text{SFT}}$  ) as common practice in many recent VLMs (e.g., [1]), ensuring that the model remains close to the reference distribution while still learning preference-based alignment to preserve generation quality.\n\n**Furthermore, we argue that some deviation from the reference model is not only acceptable, but desirable in our setting as an open research question**: Our goal is to equip the model with new capabilities (long-context reasoning) beyond the reference. Recent RLHF frameworks (e.g., DAPO [2], GLM-4.1V [3]) have removed KL-regularization between the policy model and the reference model to allow more flexible policy exploration.\n\n**References**\n\n[1] Enhancing the Reasoning Ability of Multimodal Large Language Models  via Mixed Preference Optimization\n\n[2] DAPO: An Open-Source LLM Reinforcement Learning System at Scale\n\n[3] GLM-4.1V-Thinking"}, {"review_id": "D6taLrTLs7", "reviewer": "Reviewer_vyqf", "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.", "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", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "S1: Achieves strong long-video gains with only 16 k synthetic triples and no new human labels.\n\nS2: Stage 1 addresses position bias; Stage 2 adds multi-segment reasoning, mirroring long-text LLM alignment.\n\nS3: 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.\n\nW1: \tCompeting 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.\n\nW2: More ablations needed including fine-tune InternVL-2.5-8B with your Stage 1 data without DPO to isolate DPO’s contribution\n\nW3: 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.", "was_revised": true, "final_justification": "Thank the authors for the response. My concerns have been sovled and I will consider to improve my rating score.", "rebuttal": "**Q1: Comparison of fairness with released weights**\n\nWe would like to clarify that our Stage 1 and Stage 2 training involved no additional manual annotations. All training signals are derived from the LLaVA-video-178k Caption dataset, **which is publicly available and already used as part of the InternVL-2.5-8B SFT training data [1].**\n\n**As suggested by reviewer dFSN, we report additional baseline results, including Video-LLaMA3 and InternVideo2.5, both of which have also been fine-tuned on more diversified video datasets that are either the same as or highly overlapping with LLaVA-video-178k.** Therefore, our method does not benefit from any original dataset.\n\nOur performance gains stem not from access to additional data, but rather from our method's capability to more efficiently organize and utilize existing data, through multi-stage optimization and explicit long-video reasoning.\n\n**Performance Summary**\n| Model          | LVBench     | LongVideoBench | MLVU        | Video-MME                 |\n| -------------- | ----------- | -------------- | ----------- | ------------------------- |\n| InternVL2.5   | 44.7        | 62.7           | 67.6        | 63.3 / 66.9               |\n| + LongVPO      | **50.1** (+5.4) | **66.6** (+3.9)    | **74.1** (+6.5) | **64.6** (+1.3) / **70.3** (+3.4) |\n| InternVideo2.5 | 46.4        | 60.6           | 72.8        | 63.3 / 71.1               |\n| + LongVPO      | **52.4** (+6.0) | **67.9** (+7.3)    | **74.5** (+1.7)       | **66.1** (+2.8) / **73.0** (+1.9)              |\n| Video-LLaMA3   | 45.3        | 59.8           | 73.0        | 66.2 / 70.3               |\n|+ LongVPO\t| **49.8** (+4.5)\t| **63.4** (+3.6)\t| **74.6** (+1.6)\t| **66.7** (+0.5) / **71.4** (+1.1) |\n\n**Q2: Isolating the contribution of DPO**\n\nAs shown in Figure 5 of our main paper, we conducted experiments that fine-tuned InternVL-2.5-8B using only our Stage 1 caption data. Interestingly, we observed significant performance degradation, especially due to overfitting. This is because the Stage 1 Caption data overlaps with InternVL’s own SFT data, and repeated fine-tuning on this data leads to overfitting.\n\nAblation Results (InternVL family):\n\n| Model                             | LVBench     | MLVU        | Video-MME                 |\n| --------------------------------- | ----------- | ----------- | ------------------------- |\n| InternVL2.5                          | 44.7        | 67.6        | 63.3 / 66.9               |\n| InternVL2.5-LongVPO-Stage1           | **46.8** (+2.1) | **72.9** (+5.3) | **64.2** (+0.9) / **70.1** (+3.2) |\n| InternVL2.5-SFT-Stage1-Based-Caption | 42.3        | 68.6        | 60.9 / 65.5               |\n| InternVL2.5-SFT-Stage1-Data          | 43.5        | 71.3        | 62.4 / 68.0               |\n\nWe applied a second-stage SFT using preferred responses generated from our pipeline, which alleviates overfitting and demonstrates the effectiveness of our multi-stage design. These results suggest that while data quality matters, our training strategy and pipeline design are key contributors to performance improvement.\n\n**Q3: Risk of self-reinforcement bias in Stage 2**\n\nWe understand the concern regarding self-reinforcement bias. However, under the context of limited long-video QA data, our focus is on developing a scalable, low-cost strategy to expand long-context capabilities in VLMs without relying on expensive human annotations.\n\n**1. The preference order is valid in most cases.** In Stage 2, we generate the preferred answer $y_0$ using the full video context, and construct a dispreferred answer $y_1$ using either an irrelevant scene or only a partial relevant one. Although the generated answer $y_0$ is not perfect, it tends to hallucinate less than $y_1$, which lacks the necessary context. Therefore, we argue that the preference order \n$\\( y_0 \\succ y_1 \\)$  holds true in the majority of cases.\n\n**2. Empirical evidence against reinforcement bias.** To examine whether self-reinforcement causes overfitting or degradation, we repeated Stage 2 DPO training twice iteratively using InternVideo2.5. We observed consistent performance improvement across multiple benchmarks, indicating robust generalization rather than overfitting to earlier outputs.\n\n| Model              | LVBench | LongVideoBench | MLVU | Video-MME   |\n| ------------------ | ------- | -------------- | ---- | ----------- |\n| InternVideo2.5     | 46.4    | 60.6           | 72.8 | 63.3 / 71.1 |\n| + Stage 2 (iter 1) | 51.0    | 67.2           | 74.1 | 65.6 / 72.5 |\n| + Stage 2 (iter 2) | 52.4    | 67.9           | 74.5 | 66.1 / 73.0 |\n\nThis result suggests that LongVPO has generalizable benefits, even under repeated preference sampling and DPO training.\n\n**3. On the use of external judges or human evaluation.** We agree that incorporating independent judges (e.g., Gemini 2.5 Pro or human annotators) could improve reliability. **However, such strategies would significantly increase the cost and conflict with the paper’s core goal of efficient and scalable training for long video reasoning.**\n\nOur approach aligns with the philosophy adopted by recent image-domain methods, such as MPO [1], which automatically constructed a large-scale preference dataset of over 1M pairs—mostly without the use of external judges. Although this scale is significantly larger than ours, MPO has demonstrated the effectiveness and scalability of self-training strategies on image reasoning tasks. **This supports the feasibility of similar approaches (i.e., self-training) in the long-video domain, especially under limited annotation resources.**\n\nThus, while more rigorous preference curation could further improve results, we believe our current results are sufficient to demonstrate the promise of this self-training paradigm in the long-video setting. We plan to apply further preference filtering using LLMs and captions before open-sourcing our data, and leave more fine-grained supervision or human/VLM evaluation as future work.\n\n**References**\n\n[1] InternVL2.5 Technical Report: Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling\n\n[2] Enhancing the Reasoning Ability of Multimodal Large Language Models  via Mixed Preference Optimization"}]

Author rebuttal:

Meta-review: This paper proposes LongVPO, a two-stage Direct Preference Optimization framework that enables short-context vision-language models to understand ultra-long videos without long-video annotations.
The authors provide several experimental results that convince reviewers to raise their ratings. The final ratings are all borderline acceptance. The remaining concern is the unconvinced claim that long-context VLMs experience performance degradation as input length increases may simply be due to the gap between training and testing. However, most reviewers agree that the proposed method achieves a significant performance gain compared to Direct DPO with lower computational cost. Hence, the AC recommends acceptance.
Decision: Accept (poster)