Title: Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Vision-Language Model

Multimodal LLM

Multimodal Learning

Data Strategy
Abstract: We introduce Eagle2.5, a frontier vision-language model (VLM) for long-context multimodal learning. Our work addresses the challenges in long video comprehension and high-resolution image understanding, introducing a generalist framework for both tasks. The proposed training framework incorporates Automatic Degrade Sampling and Image Area Preservation, two techniques that preserve contextual integrity and visual details. The framework also includes numerous efficiency optimizations in the pipeline for long-context data training. Finally, we propose Eagle-Video-110K, a novel dataset that integrates both story-level and clip-level annotations, facilitating long-video understanding. Eagle2.5 demonstrates substantial improvements on long-context multimodal benchmarks, providing a robust solution to the limitations of existing VLMs. Notably, our best model Eagle2.5-8B achieves 72.4\% on Video-MME with 512 input frames, matching the results of top-tier commercial model such as GPT-4o and large-scale open-source models like Qwen2.5-VL-72B and InternVL2.5-78B.

Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models

Abstract

We introduce Eagle2.5, a frontier vision-language model (VLM) for long-context multimodal learning. Our work addresses the challenges in long video comprehension and high-resolution image understanding, introducing a generalist framework for both tasks. The proposed training framework incorporates Automatic Degrade Sampling and Image Area Preservation, two techniques that preserve contextual integrity and visual details. The framework also includes numerous efficiency optimizations in the pipeline for long-context data training. Finally, we propose Eagle-Video-110K, a novel dataset that integrates both story-level and clip-level annotations, facilitating long-video understanding. Eagle2.5 demonstrates substantial improvements on long-context multimodal benchmarks, providing a robust solution to the limitations of existing VLMs. Notably, our best model Eagle2.5-8B achieves 72.4% on Video-MME with 512 input frames, matching the results of top-tier commercial model such as GPT-4o and large-scale open-source models like Qwen2.5-VL-72B and InternVL2.5-78B.

1 Introduction

Despite the significant advances in multimodal learning [4, 16, 52, 53, 97], many vision-language models (VLMs) remain focused on short-context tasks, with long-context understanding underexplored. This gap is particularly evident in both long video comprehension and high-resolution image/video understanding, where the processing of extended visual contexts remains an open challenge. Such extended contexts encompass multiple images, extended video sequences, highresolution media, or combinations thereof. However, the development of long-context VLMs is still in its early stages, hindered by fundamental challenges in dataset construction, architecture design, training strategies, and computation/memory bottlenecks.

To enable long-context visual understanding, several approaches have been proposed to address the challenge of processing extended visual inputs by designing specialized compression or selection modules [52, 84, 57, 45, 43, 117, 103, 53]. While these methods effectively circumvent the need to extend the context length of VLMs, they often introduce additional computational overhead or capacity limitations, potentially constraining model performance. A promising research direction is to extend the context length of LLMs to enable native long-context understanding. While prior studies [110, 126, 85] have explored this direction, challenges and key limitations still remain. First, the performance of existing methods is often suboptimal, generally falling behind proprietary models. Second, these approaches struggle to achieve consistent improvements as the amount of visual input

increases. Lastly, the optimal training strategies for state-of-the-art long-context VLMs remain unclear, given the complex interplay of factors such as training strategies and data recipes.

To this end, we present Eagle 2.5, a versatile multimodal model designed to efficiently process extensive contextual information. Unlike models solely optimized for handling long multimodal sequences without improving performance, Eagle-2.5 benefits from increased input length, leading to consistent performance gains besides merely accommodating longer inputs. As shown in Fig. 1, our model achieves superior context coverage and exhibits consistent performance scaling with increasing frame counts. Notably, it attains competitive results compared to larger models such as GPT-4o [77] and Qwen2.5-VL-72B [5], while maintaining a significantly smaller parameter footprint.

Eagle 2.5 is driven by both the advanced training strategy and data recipe. For training strategy, we introduce two core components for effective long-context learning: information-first sampling and progressive training.

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Information-first sampling . The information-first sampling strategy ensures the preservation of essential visual and semantic information through two mechanisms: (1) Image Area Preservation , which optimizes tiling to retain the majority of the original image area while maintaining aspect ratio fidelity, avoiding rigid aspect ratio constraints; and (2) Automatic Degradation Sampling (ADS) , which dynamically balances visual and textual inputs by prioritizing complete text retention while adaptively optimizing visual content to maximize context length utilization and preserve multimodal information.

Progressive training . We employ a progressive mixed post-training approach, wherein context length is incrementally expanded during training, enhancing the model's ability to process inputs of varying sizes. This integrated strategy significantly improves information density over static sampling methods while ensuring consistent performance across diverse input types and lengths.

For data recipe, we embrace the ' diversity first, then quality ' principle in curating the training data pool. Our data recipe combines open-source data (including human-annotated data as well as synthetic video data) with our self-curated Eagle-Video-110K dataset, specifically designed to enhance long video understanding capabilities. We adopt a diversity-driven collection strategy, using multiple video sources and a similarity thresholding method to identify novel clips that maximize content diversity. Our dataset is distinguished by its dual annotation approach:

A top-down story-level method that leverages human-annotated chapters as meaningful segments instead of traditional shot-level segmentation, producing dense captions that form the basis for comprehensive long-form QA pairs capturing the entire video's narrative structure;

A complementary bottom-up clip-level approach that generates focused QA pairs for short clips using GPT-4o with diverse question types. To address the challenge of extending localized clip annotations to full-length videos, we implement anchors that incorporates temporal references and contextual elements without revealing answers, thereby letting models understand both overarching narratives and precise spatio-temporal details within videos.

2 Related Work

Vision-language models. Advancements in large language models (LLMs) [20, 1, 77] have significantly propelled visual understanding by integrating visual features, leading to the creation of Visual Language Models (VLMs) [52, 76, 58, 133]. Open-source VLMs [58, 60, 49, 97, 92, 18, 66, 50, 114, 67, 39] continue to achieve breakthroughs, often matching or exceeding the performance of state-of-the-art commercial models like GPT-4V/4o [77] and Gemini-1.5 [83]. The release of open-source VLMs [49, 95, 40, 38], complete with its training data and code base, has further

accelerated research in this area. However, most current VLMs primarily focus on short-context understanding, handling only a few images or short video clips at a time. Eagle 2.5 advances this field by concentrating on long-context visual understanding through a comprehensive exploration and development of training strategies and data recipes.

Long-context VLMs. Long-context VLMs were developed to address the challenges of processing large multimodal sequences. Currently, methods for long-context VLMs fall into two main categories. The first category involves specialized modules designed for context compression. Question-guided compressions [84, 57, 45, 98] or selection [117, 55, 116] methods extract question-related visual cues through an additional module, while various token reduction techniques [43, 117, 103, 53, 57, 124, 102, 62, 62, 61] aim to minimize the visual representation before LLM processing. The other category attempts to directly extend the context of LLMs. Works like LongVA [126], LongVILA [110], and LongViTA [85] extend the context length of LLMs to accommodate longer multimodal sequences. While promising, these approaches often underperform proprietary models, fail to show consistent performance improvements with increasing visual input, and have underexplored constraints on training strategies and data recipes. Our approach focuses on developing native long-context capabilities that enhance VLMs by exploring training data, formulations, and without introducing additional compression modules or suffering from performance inconsistencies observed in previous expansion attempts.

Long-context multimodal data. To enhance VLMs' long-context multimodal understanding capabilities, various datasets have been proposed. Some datasets focus on multimodal understanding of long documents [89, 96, 94, 80], such as slides and papers. However, they often lack temporal understanding. Other datasets [36, 105, 24, 82, 87, 88, 120, 121] emphasize the temporal coherence and information retrieval across long spans inherent in movies. Additionally, recent datasets [73, 13, 30, 129] covering domains further enhance VLMs' long-context multimodal understanding. Regarding the annotation methods for long-context multimodal datasets, early works [89, 96, 94, 36, 87, 73] relied on manual efforts. To reduce costs, some methods [80, 24, 82, 88, 120, 121, 30, 13, 129] use tools like GPT-4V [76] and Gemini [91] for automated or semi-automated annotation. Recent advancements in data construction emphasize hierarchical annotation strategies [30], which can preserve narrative structure in long videos. These advancements reflect a trend towards creating balanced datasets that effectively assess long-context multimodal understanding while managing creation costs.

3 Method

This section introduces the model architecture, training strategies, and data recipe of Eagle2.5.

3.1 Model Architecture

We design our proposed model as a versatile multimodal system capable of efficiently processing longcontext information, rather than a specialized model solely optimized for handling extended multimodal inputs. To ensure adaptability and generalization across diverse tasks, we deliberately avoid incorporating tailored compression modules that might constrain the model's flexibility. Following the architecture of LLaVA [58], we employ an MLP projection layer to align vision embeddings from SigLIP [123] with the LLM representation space, as shown in

Information First Sampling

SigLIP-so400M

MLP connector

LLM

Text

Fig. 2. In this work, we utilize the Qwen2.5 series models [93]. To effectively handle any-resolution images, we adopt the image tiling strategy, inspired by LLaV A-1.5 [59] and InternVL [16].

3.2 Training Strategy

Our approach contains two key components to achieve effective long-context training: first, an information-first sampling strategy that establishes optimal sampling criteria; and second, a progressive training schedule based on this strategy, which directs the entire model training process.

3.2.1 Information-First Sampling

In multimodal training, the sampling of visual content is essential. Multi-image documents typically comprise dozens of pages with ultra-high-resolution images, while video content can vary drastically in length - from mere seconds to hours. To effectively manage this diversity, we present informationfirst sampling to promote information preservation from both visual and semantic dimensions.

Image area preservation (IAP). Traditional tiling methods divide an image of size W × H into a rigid r w × r h grid of s × s tiles. While effective for handling high-resolution inputs, these approaches often distort the original image geometry through improper aspect ratio handling. For example, InternVL [16] imposes strict aspect ratio constraints that force image downsampling, undermining the purpose of tiling. To address this, we propose an area-prioritized tiling strategy that optimizes two key objectives:

Area Preservation : Encourage maintaining at least 60% of the original area ( A orig = WH ) in the tiled version ( A new = r w r h s 2 ).

Aspect Ratio Fidelity : Align the tiling ratio r t = r w /r h with the original aspect ratio r orig = W/H .

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For candidate tiling ratios { ( r w , r h ) | r w × r h ≤ N } , we select the optimal configuration by:

This formulation imposes penalties when A new < 0 . 6 A orig but avoids over-rewarding configurations where A new > 0 . 6 × A orig. The aspect ratio term reaches maximum value 1 when r t = r orig, decaying symmetrically for deviations. A comparison between the strategies is shown in Fig. 3.

Automatic degradation sampling. VLMs require careful allocation of sequence length budgets between visual and textual inputs. Conventional vision-context-centric approaches sample visual content (e.g., video frames) at fixed rates or with predetermined counts, risking text truncation and suboptimal token allocation. We propose Automatic Degradation Sampling ( ADS ), an all-contextcentric strategy that dynamically optimizes this balance.

Given a training sample S = { S visual , S text } with max sequence length L max , where S visual contains arbitrary combinations of images, videos, and multi-page documents: 1) Compute fixed text token length L text; and Derive fixed visual token budget: L visual = L max -L text. Thus, we keep the complete textual information by constricting the visual token budget.

For visual content optimization under L visual, we distinguish two types and optimize two key variables:

Images : Optimize maximal tile count per image t to maximize spatial information of M images.

Temporal content (video/doc): Optimize sampling count n to maximize temporal coverage.

The constrained optimization problem is formulated as:

Where optimization variables are the tile count per image ( t ) and temporal sampling count ( n ), with fixed parameters including: total image instances M (calculated from input), token function L ( t, I i )

used to calculate the tokens of i -th image I i under maximal tiling number t , predefined upper bounds T max = 12 (max tiles per image) and N max = 2 × duration / 1 × pages (video/doc constraints). For temporal content, we do not use image tiling, thus the token quantity per temporal unit (frame/page) is L (1 , · ) = 256 .

Given that training samples typically exhibit mutually exclusive composition (predominantly images or temporal content), ADS employs a dual-phase degradation process to address the above optimization problem:

Temporal degradation : Initially, we fix the max tile number t = 1 and focus on temporal sampling. We target a sampling rate of 2 FPS for videos, and the usage of all images for multi-image documents. We also require that each visual input has at least N min frames; if this minimum cannot be met within the visual context budget, the sample is discarded. Formally, the maximally sampled temporal units is n ∗ = ⌊ L visual -M × 256 256 ⌋ .

Tiling degradation : After deciding the number of frames, we dynamically adjust the tiling to maximize the use of available context. Let T = { 12 , 8 , 6 , 4 , 2 , 1 } represent the possible tile configurations in decreasing order. We choose the highest tile configuration t ∗ such that: t ∗ = max { t ∈ T : ∑ m i =1 L ( t, I i ) ≤ ( L V -n ∗ × 256) } This strategy preserves as much visual detail as possible while ensuring the full textual input is retained, thereby optimizing the overall learning signal.

This dual-phase approach guarantees complete text preservation while dynamically adapting visual resolution to available context budget, achieving superior information density compared to static sampling strategies.

3.2.2 Post-Training Schedule

We introduce a comprehensive post-training framework consisting of two complementary strategies. First, we establish a foundational mixed post-training approach, upon which we develop an enhanced progressive mixed post-training strategy to substantially improve model performance across varying context lengths.

Mixed post-training. Since the model needs to efficiently process multimodal inputs of diverse lengths, maintaining consistent performance across variable context sizes is essential. Our ADS method adaptively adjusts each training sample to the maximum sequence length L max, providing a frame-agnostic training paradigm. We implement a mixed training strategy with length-balanced packing [3] to optimize performance uniformly across the entire spectrum of context lengths.

Progressive mixed post-training. For scenarios with large L max values, balancing the distribution of long and short sequences becomes computationally intensive, and achieving optimal performance through a single training iteration proves challenging. To address this limitation, we propose a progressive mixed training methodology that gradually exposes the model to increasingly larger L max values, systematically enhancing its capacity to process extended contexts. Compared to conventional mixed training, our method more effectively preserves the model's capabilities across different sequence lengths while safely generating diverse model variants at intermediate training stages. In our exeriment, we sequentially set L max to 32K, 64K and 128K.

3.3 Data Recipe

Our data recipe begins with open-source data. We embrace the 'diversity first, then quality' principle and gather data from various open sources. This data mainly comprises high-definition multiimage/short videos, long videos, multi-page documents, and extensive text data. We also find that current open-source video data often lacks sufficient length. We thus propose a novel dataset, Eagle-Video-110K, to complement the length, as shown in Fig. 4.

3.3.1 Open-Source Long-Context Data

A model's capability is intrinsically linked to the diversity of its training data. Thus, gathering the most diverse data possible represents a core principle of this work, leading to two main strategies:

Category

Dataset

Video Classification

Kinetics710 [9, 101], Something-Something-v2 [25], ActivityNet [8], HACS Segment [130], COIN [90],

HIREST [122], FineAction [63], PortraitMode-400 [31]

Temporal Action Localization

ActivityNet

[8],

HACS Segment [130],

FineAction [63],

Ego4D-MQ [26], COIN [90],

HIREST [122],

Perception-Test [79]

Video Temporal Grounding

Charade-STA [23],

QVHighlight [48], Ego4D-NLQ [26], Didemo [32], QueryD [75], MedVidQA [29],

Youcook2 [132], FineVideo [22], ActivityNet [8], HACS Segment [130], FineAction [63], Ego4D-MQ [26],

COIN [90], HIREST [122], Perception-Test [79], EgoExoLearn [37]

Dense Video Captioning

ActivityNet [8], Youcook2 [132], EgoExoLearn [37], ViTT [35], HIREST [122], COIN [90]

Temporal Segmentation

Breakfast [46], ViTT [35]

Temporal Reasoning

ActivityNet-RTL [34]

General Video QA

TVQA [47],

CLEVRER [115],

NextQA [108],

SportsQA

[51],

LLaVA-Video

[128],

FineVideo

[22],

VideoGPT+ [69], Oops [21], Perception-Test [79], EgoTaskQA [42], CinePile [82], STAR [104]

Multi-Page Document

SlideVQA [89], DUDE [96], MP-DocVQA [94]

Video Captioning

ActivityNet

[8],

Youcook2

[132],

Shot2story

[30],

Vript

[112],

LLaVA-Video [128],

Momentos [99],

FunQA [109], S-MiT [74], LLaVA-Hound [127], Ego4D-HCap [41], EgoExoLearn [37]

Long Text

LongAlign [6], LongReward [125]

Human-annotated Data: We integrate various open-source human-annotated datasets, including established video and image-document collections such as COIN [90] and SlideVQA [89], which can be directly considered as high-quality data.

Synthetic Video Data: Considering that videos naturally contain long-context information, we incorporate open-source synthetic video data, such as LLaVA-Video [128]. These datasets are primarily annotated automatically using state-of-the-art models including GPT-4V/4o [76, 77], Claude-3 [2], and Gemini-1.5 Pro [83].

Combined with short-context data, all collected open-source datasets are summarized in Tab. 1. For convenience, we refer to this collective dataset as Open-Data.

3.3.2 Eagle-Video-110K

We curate Eagle-Video-110K to enhance long video understanding capabilities. Specifically, we first collect videos using a diversity-driven strategy. We then automatically annotate these videos using both top-down and bottom-up approaches to generate comprehensive story-level and fine-grained clip-level annotations, as shown in Fig. 5.

Diversity-driven video collection. We utilize several data sources for our video collection: Vidchapters [111], MiraData [44], InternVid10M [100], Panda-70M [14], Vript [112], Shot2story [30], ViTT [35], and WebVid10M [7], collectively referred to as A . Our approach prioritizes diversity, focusing on gathering a wide range of video content. For the current training dataset B , we use CLIP [81] to extract temporal features at a rate of 1 frame per second. Videos from both A and B are segmented into 10-second clips. We perform a pooling operation on each clip's frames to derive a representative feature vector. Let { b i } N B i =1 represent the clips from B ,

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and { a j } N A j =1 represent those from A . We calculate the pairwise cosine similarity between clips from B and A . For each clip a j in A , we identify its maximum similarity with any clip in B : S max ( a j ) = max 1 ≤ i ≤ N B S ( b i , a j ) We then introduce a similarity threshold τ = 0 . 5 . Clips in A with S max ( a j ) below this threshold are considered most novel relative to B : A novel = { a j ∈ A | S max ( a j ) < τ }

The clips in A novel and their original videos are selected to enhance the diversity of our collection.

Story-level video data. We construct story-level annotations for long videos using a top-down approach. Unlike existing video datasets such as Shot2story [30], which employs shot detection to segment videos and construct storylines across shots, our methodology differs fundamentally. Shotlevel segmentation often results in over-segmentation, producing excessively detailed annotations that are suboptimal for constructing coherent story-level text. Instead, we leverage human-annotated chapters as video segments, which provide more semantically meaningful annotations. We incorporate

Human annotator

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texture context anchor

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Bottom-up Approach

Top-down Approach

content from ViTT [35] and Vidchapters [111] among the selected videos and filter out any videos with fewer than two chapters to ensure they serve as effective story-level sources.

Chapter-level dense caption. For a video divided into N clips, where each clip spans from timestamp a to b , we perform visual captioning for each segment individually. For each segment, frames are sampled at a rate of up to 2 frames per second, with a maximum of 50 frames. These sampled frames, together with user-provided segment titles, guide GPT-4o [77] in generating detailed visual descriptions focused on the content indicated by the titles.

Long-form QA generation. Once visual descriptions for all segments are completed, we compile the captions for the entire video along with their corresponding time intervals and chapter titles. This aggregated information is provided to GPT-4 [1], which generates diverse question-answer pairs covering multiple question types.

Clip-level video data. Story-level video data typically emphasizes high-level semantic information that unfolds over extended periods. However, for general queries, it is often necessary to focus on localized spatiotemporal details. To address this need, we propose a bottom-up , computationally efficient automatic annotation method. This approach enables the generation of short clip annotations and facilitates the conversion of segment-level annotations into video-level ones by incorporating temporal and contextual anchors.

Clip-level video QA generation. We generate QA pairs for each short clip in dataset A based on various question types. Specifically, we sample frames from each short clip at a rate of up to 2 frames per second and input them into GPT-4o. From a predefined question type pool, we randomly select five question types and prompt the model to generate corresponding questionanswer pairs.

Clip-to-video QA conversion. Since annotations for individual clips are designed for localized queries, conflicts may arise when these queries are extended to the entire video. To address this issue, we introduce two types of anchors for each clip-question pair: (1) We directly incorporate time intervals into questions to establish temporal references; (2) Using GPT-4o, we generate textual context anchors that provide additional information without revealing the answers.

4 Experiments

4.1 Comparison with State-of-the-Art VLMs

Video benchmarks. As shown in Tab. 2, Eagle2.5-8B demonstrates strong performance across multiple video understanding benchmarks. It achieves 74.8 on MVBench [54], 82.0 on Percep-tion_test [78] and 72.2 on EgoSchema, outperforming similar-sized models like InternVL2.5-8B [92] (72.0, -, -) and Qwen2.5-VL-8B [97] (69.6, 70.5, 65.0). Eagle2.5-8B excels in MLVU [131] (77.6) and LongVideobench [106] (66.4), surpassing even InternVL2.5-78B (75.7, 63.6). For VideoMME (w/o subtitle), the performance of Eagle 2.5 (72.4) significantly surpasses models of the same size and is extremely close to the 72B parameter model. On CG-Bench [11], it scores 55.8, 46.6, 45.6, 13.4 across metrics, exceeding Claude-3.5-Sonnet [2] (56.5, 40.3, 35.6, 4.17) and Gemini-1.5-Pro [83]

(50.9, 37.8, 28.7, 3.85). With 44.5 on HourVideo [10] dev set and 41.8 on test set, all surpassing Gemini-1.5-Pro [83]. Finally, on Charade-STA [23], Eagle 2.5 outperforms other models significantly, demonstrating strong temporal perception capabilities. Eagle2.5-8B shows effective long-form video understanding, highlighting its robust visual reasoning using less parameters.

Image benchmarks. As shown in Tab. 3, Eagle2.5-8B demonstrates competitive performance across diverse image understanding benchmarks. It achieves strong results on document understanding (94.1 on DocVQA [71]), chart interpretation (87.5 on ChartQA [70]), and general information extraction (80.4 on InfoVQA [72], 83.7 on TextVQA [86]). The model also performs well in optical character recognition with 869 on OCRBench [65], comparable to other models in its category. Eagle2.5-8B shows balanced capabilities across multimodal general perception and reasoning tasks, scoring 66.2 on MMstar [12], 76.7 on RWQA [107], and 81.7 on MMB 1 . 1 [64], and 62.9 on MMVet [118]. Its performance extends to knowledge domain (55.8 on MMMU [119], 84.5 on AI2D [33]), visual hallucination benchmark (54.7 on HallB [27]), and mathematical reasoning (67.8 on MathVista [68]). Overall, Eagle2.5-8B achieves a competitive 75.6 average score, demonstrating its effectiveness as a versatile vision-language model that balances performance across various visual understanding tasks.

4.2 Ablation Studies

In this section, we conduct experiments on various benchmarks to evaluate our method. We mainly design experiments to study the following questions.

Q1: How do video and image data influence each other's benchmarks? Tab. 4 studies the impact of long context data on the image benchmark performance. We compare the image benchmark performance without training with long-context data and with training long-context data under different L max . The results show that increasing the long-context data, under our training recipe, does not harm the short-context images and even slightly benefits it. To assess the impact of image data and pre-training on video benchmarks, we conduct a comparison using the L max = 32 K model.

Table 5: The impact of image data and pretraining on the performance of video benchmarks. S1/S1.5 denotes the stage-1 and stage-1.5 similar to Eagle2 [3]. 'OD' is short for Open-Data.

Table 6: The impact of information-first sampling on performance of image and video benchmarks. The baseline is equipped with IAP and ADS strategy. 'PT' is short for PereptionTest.

For each benchmark, we sampled at 2FPS, ensuring a maximum of 32 frames. As shown in Tab. 5, extensive image pre-training significantly enhances performance on short video benchmarks like MVBench, as well as on the relatively simple long video benchmark, MLVU. However, for the more challenging and held-out long video benchmark, Video-MME, the improvements are less pronounced.

Q2: The effect of information-first sampling on performance? Tab. 6 illustrates the impact of the information-first sampling strategy on image and video tasks. Without the Image Area Preservation strategy, high-resolution image benchmarks like InfoVQA and fine-grained video benchmarks such as Perception-test suffer significant performance degradation. The effect on other benchmarks is less pronounced. While the Automatic Degradation Sampling strategy offers convenience for processing various visual inputs, experiments indicate that omitting it poses a risk. The vision-context-centric strategy may truncate supervision signals, leading to performance loss.

Q3: The impact of different post-training schedules? Tab. 7 illustrates the performance impact of progressive mixed training from 32K to 64K compared to direct 64K mixed training on the video benchmarks. The results demonstrate that progressive training outperforms direct 64K mixed training, possibly due to two reasons: 1) Direct 64K hybrid training disperses samples across the 64K space, diluting the focus on shorter contexts.

2) Some longer samples are challenging to learn without a gradual learning process that transitions from easy to difficult. Fig. 6 shows the effect of progressive mixed training on the Video-MME benchmark. It reveals that as progressive training advances, the model's capacity to process more frames is gradually enhanced.

Q4: The impact of Eagle-Video-110K data on performance? We assess the impact of EagleVideo-110K on model performance. Table 7 shows that it improves several long and short video benchmarks. Figure 6 demonstrates that training with Eagle-Video-110K enhances the model's ability to process many frames ( ≥ 128 frames) by incorporating long videos absent from the opensource training dataset.

5 Conclusion

In this work, we present Eagle 2.5, an advanced vision-language model family designed for longcontext multimodal understanding. Through inno-

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vative training approaches - including information-first sampling and progressive mixed post-training - combined with our dual-annotated Eagle-Video-110K dataset, we significantly enhance long-context

comprehension capabilities. Eagle 2.5 achieves leading performance on video and high-resolution image benchmarks, matching larger models like GPT-4V and Gemini 1.5 Pro despite its smaller size. With advanced training strategies and diverse data, Eagle 2.5 sets a strong foundation for future research, paving the way for efficient and versatile VLMs in complex real-world scenarios.

Limitations. The training of Eagle2.5 required substantial computational resources, specifically a cluster of 128 H100 GPUs. This high resource demand may limit the reproducibility and accessibility of our approach for researchers or practitioners without access to large-scale GPU infrastructure. Future work could explore more resource-efficient training strategies or model architectures to reduce computational requirements.

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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: The abstract and introduction clearly state the main claims: Eagle2.5 is a frontier vision-language model (VLM) for long-context multimodal learning, introducing a generalist framework for both long video comprehension and high-resolution image understanding. The paper claims the introduction of two new techniques (Automatic Degrade Sampling and Image Area Preservation), a new dataset (Eagle-Video-110K), and substantial improvements on long-context benchmarks, with performance matching or surpassing top-tier commercial and open-source models. These claims are supported by the theoretical and experimental sections, and the scope is well-aligned with the results presented in the paper.

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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.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

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:

Guidelines:

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

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Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: The paper provides detailed descriptions of the model architecture, training strategies, data recipes, and benchmark evaluation protocols. It specifies the datasets used, the training schedule (including context lengths), and the evaluation settings (e.g., sampling rates, frame counts). While code and data are not directly linked in the main text, the methods and procedures are described in sufficient detail for an expert to attempt reproduction of the main results.

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The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

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(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

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(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [No]

Justification: There is no explicit statement in the main paper about releasing code or data, nor are URLs or instructions provided for accessing the Eagle2.5 model, EagleVideo-110K dataset, or scripts to reproduce experiments. The paper summarizes data sources and contributions but does not provide open access or reproducibility instructions as supplemental material.

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Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: The paper provides extensive details about the experimental setup, including data splits, sampling rates, frame counts, tiling parameters, and training schedules (e.g., progressive training with specified context lengths). Tables specify which datasets are used for which tasks, and ablation studies clarify the impact of different settings. Hyperparameters and optimizer choices are not exhaustively listed, but the level of detail is sufficient for understanding and contextualizing the results.

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The answer NA means that the paper does not include experiments.

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The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [No]

Justification: The paper reports benchmark scores and ablation results in tables but does not include error bars, confidence intervals, or statistical significance tests for the reported results. There is no discussion of variability, standard deviation, or the statistical robustness of the findings.

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The answer NA means that the paper does not include experiments.

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For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: The paper does not provide information about the compute resources used for training or evaluation (e.g., type of GPUs, number of nodes, training time, or memory requirements). This information is absent from both the main text and tables.

Guidelines:

The answer NA means that the paper does not include experiments.

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The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

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10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: The paper does not include a discussion of broader societal impacts, either positive or negative. There is no section or paragraph addressing potential misuse, fairness, privacy, or security implications of releasing a powerful long-context VLM or dataset.

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The answer NA means that there is no societal impact of the work performed.

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Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a frontier multi-modal large language model named Eagle2.5, which focuses on long-context multi-modal learning. Eagle2.5 is a generalist framework to addresses the challenges in long video comprehension and high-resolution image understanding. It proposes Automatic Degrade Sampling and Image Area Preservation to preserve contextual integrity and visual details. Also, the work also proposes a diverse and high-quality dataset named Eagle-Video-110K to facilitating long-context training. Extensive experiments demonstrate the effectiveness of Eagle2.5.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper proposes a new frontier multi-modal large language model to address long-context modeling, and their experiments have demonstrated the effectiveness of the proposed model. Thus, I think this paper does make some contributions. \n\nHowever, I have some concerns about the techniques and experiments, which are listed as follows:\n- In my understanding, the proposed Image Area Preservation involves more patches/tokens from the the original videos/images, such that it would cover more complete content in videos/images. However, it will surely involve more computation cost. Have you analyze the budget? \n- Eagle-Video-110K involves synthetic video data, whose captions are automatically generated by advanced MLLMs like GPT-4o and Gemini-1.5-Pro. Have you make some efforts to ensure the quality of generated captions as these MLLMs have hallucinations. \n- In Line 245, why you use CLIP? Could you please give me some reasons? Have you try any other encoders?\n- I think it will be better that you can highlight the best/second-best results in experiment tables, e.g., Table 2 and 3.\n\nQUESTIONS:\nIn addition to the questions in Weakness part, I have another important question. \n\nIn Table 3, Eagle2.5 does not perform better than Qwen2.5-VL-8B in terms of the overall average metric. For example, Eagle2.5 is inferior to Qwen2.5-VL-8B on DocVQA. Could you please show me some insights or conjectures about the reasons of these phenomena? I think some explanation or analysis will surely improve the quality of the paper.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nEagle2.5, a Vision-Language Model (VLM) aimed at advancing long-context multimodal understanding. The authors tackle the challenges of processing long videos and high-resolution images by introducing a novel training framework and dataset. The core contributions are threefold: An \"Information-First Sampling\" strategy to dynamically balance and preserve visual and textual data, a \"Progressive Mixed Post-Training\" schedule that incrementally increases the model's context length , the Eagle-Video-110K dataset, which provides much-needed long-form video content with a dual-annotation approach for both narrative and detail. Through extensive experiments, the 8B-parameter Eagle2.5 is shown to achieve state-of-the-art performance, often matching or outperforming significantly larger models.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n1. Significance: The paper tackles a highly relevant and challenging frontier in multimodal AI: long-context visual understanding.\n2. Noval Training Strategy and Data Curation: The paper introduces a well-motivated progressive training schedule to effectively scale context length. Furthermore, the development of the Eagle-Video-110K dataset addresses a recognized scarcity of long-form video data for training, and its dual annotation strategy is a thoughtful approach to capturing both semantic and temporal details.\n3. Rigorous Evaluation: The claims are substantiated by extensive experiments across a wide array of benchmarks and detailed ablation studies that validate the effectiveness of each component of the proposed framework.\n4. Competitive Performance: The 8B model demonstrates strong empirical results, achieving performance on par with or exceeding much larger open-source and proprietary models.\n5. Clarity and Organization: The paper is well-written, logically structured, and easy to follow. The authors clearly articulate the problems with existing long-context VLMs and present their solutions in a coherent manner.\n\n**Weaknesses:**\n1. Lack of Qualitative Analysis: The paper would be strengthened by a qualitative analysis of its core sampling strategies. It currently lacks case studies or visualizations to provide intuition for how IAP and ADS make decisions. For example, showing how an image is tiled by IAP or which frames are dropped by ADS in a given scenario would offer valuable insight into their practical behavior.\n2. The dataset proposed in the article holds significant importance, as it appears to contribute most noticeably to the improvement in results. However, the manuscript does not seem to provide a clear plan for the dataset's release, which could impact the reproducibility and overall significance of the work.\n\nQUESTIONS:\n1. Regarding the Nature of IAP/ADS:The IAP and ADS methods are query-agnostic, pre-processing heuristics, applied before the model sees the user's actual question. Given that the optimal strategy for information retention is highly context-dependent , how does this approach mitigate the risk of discarding crucial visual details before their relevance can be assessed in light of the specific question being asked? How to prove this heuristic actually preserve more information than direct sample and tiling method? Is it necessary to utilize this sampling strategy during training for it to be effective at inference, or could it be applied post-hoc to other pre-trained VLMs?\n2. Regarding Analysis of IAP/ADS: To better understand the behavior of the sampling strategies, would it be possible to provide a specific case study? For instance: A visualization of a high-resolution image tiled using IAP versus a baseline method. A quantitative example of the trade-off made by ADS for a specific long-video sample that contains lengthy text, showing how many frames are preserved versus discarded. \n3. Is there a clear plan to open-source the methods and dataset? I believe this could greatly benefit the field.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces Eagle 2.5, an 8 B-parameter vision-language model (VLM) aimed at native long-context reasoning across both video and high-resolution image domains. Key ingredients are:\n\n1) **Information-First Sampling (IFS)** – consisting of Image-Area Preservation (IAP) to keep ≥ 60 % of the original pixels during tiling, and Automatic Degradation Sampling (ADS) that dynamically trades visual tokens for text tokens so the text prompt is never truncated.\n\n2) **Progressive training** – the context window of the model is gradually expanded, which the authors show yields better length-robustness than a single long-window run.\n\n3) **Eagle-Video-110 K**, a 110 k-clip dataset with dual story-level and clip-level annotations to enhance long-form video understanding capabilities.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1) **Timely focus on long-form video understanding.** Scaling vision-language models to hour-long content is a pressing challenge; meaningful progress here benefits both research and downstream applications.\n\n2) **Valuable resource.** The Eagle-Video-110 K dataset (≈110 k clips with story- and clip-level annotations) should become a useful test-bed for future work on extended-duration reasoning and retrieval.\n\n3) **Impressive efficiency/accuracy frontier (Table 2).** The proposed 8B parameter model matches or beats far larger models on Video-MME, LVBench, and, notably, outperforms Gemini 1.5 on the challenging HourVideo benchmark.\n\n\nWeaknesses:\n1) **HourVideo breakdown:** Could you provide per-category results for HourVideo (analogous to Table 2 in the HourVideo [A] paper)? This would clarify whether gains come primarily from visual reasoning, or other tasks such as summarization or perception.\n\n2) **Reproducibility & release plan:** Will you release all training code, model weights, and the full Eagle-Video-110 K dataset to the community? \n\n3) **Terminology clarity:** “Information-First Sampling” is not a standard terminology. A brief justification would improve clarity.\n\n4) **Standard deviations missing:** No variance metrics are given. Please include standard deviations for a representative subset of experiments.\n\n5) Missing related work on long-form video understanding: [B]\n\nThought experiment: Do you think long-form video understanding is fundamentally a long-context modelling problem?\n\n==\n\n[A] Chandrasegaran, Keshigeyan, et al. \"Hourvideo: 1-hour video-language understanding.\" Advances in Neural Information Processing Systems 37 (2024): 53168-53197.\n\n[B] Ye, Jinhui, et al. \"Re-thinking temporal search for long-form video understanding.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\nQUESTIONS:\nPlease see Strengths and Weaknesses Section for a list of questions.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Eagle 2.5, a vision-language model (VLM) designed for long-context multimodal understanding. It systematically studies training strategies and data recipes for this setting, introducing methods such as information-first sampling with image area preservation and automatic degradation sampling, as well as progressive mixed post-training to enhance long-context capabilities. Additionally, the authors curate Eagle-Video-110K, a novel long-video dataset with combined story-level and clip-level annotations. Experiments show that Eagle 2.5 achieves competitive performance across a range of image and video understanding benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The paper provides a detailed description of the training strategies and data recipe for building an effective framework for long-context VLMs, which will benefit future community work in this area.\n\n2. The proposed Eagle 2.5 framework achieves strong results, demonstrating competitive performance across a variety of image and video understanding benchmarks.\n\n3. The paper includes detailed ablation studies that validate the effectiveness of the proposed components, studying the impact of elements such as information-first sampling, post-training schedules, and the Eagle-Video-110K dataset.\n\nWeaknesses:\n\n1. While the data and training strategies are well-executed, the core model architecture follows existing VLM designs. The novelty of the work is relatively limited. \n\n2. Continuing from the previous point, the paper does not appear to mention what base model Eagle2.5-8B is built upon, and there is also no baseline performance reported for this base model in the experiments. This makes it difficult to assess what really contributes to the superior performance of Eagle, especially since the paper discusses many engineering efforts, which feels a bit messy without a clear baseline for reference.\n\n3. Based on Table 7, it appears that training with the Eagle-Video-110K dataset brings only marginal improvements for long-video benchmarks (comparing the first and third rows). In Figure 6, the improvements from Eagle-Video-110K under the 64K context length setting also appear to be quite limited. It would be helpful if the authors could help understand this observation and clarify under what conditions Eagle-Video-110K contributes more.\n\nQUESTIONS:\nPlease check the questions in the weaknesses above.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-06"}