FrameThinker: Learning to Think with Long Videos via Multi-Turn Frame Spotlighting
Authors: Zefeng He
Xiaoye Qu
Yafu Li
Siyuan Huang
Daizong Liu
Yu Cheng
Venue: ICLR 2026 Poster (2026)
Keywords: LVLMs
Video Reasoning
Reinforcement Learning
Primary area: foundation or frontier models, including LLMs
TL;DR: FrameThinker is a framework that equips LVLMs with iterative frame selection for long video reasoning, achieving state-of-the-art accuracy with fewer frames.

Abstract: While Large Vision-Language Models (LVLMs) have achieved substantial progress in video understanding, their application to long video reasoning is hindered by uniform frame sampling and static textual reasoning, which are inefficient and struggle to handle visually intensive video tasks. 
To overcome these challenges, in this paper, we introduce 
the concept of thinking with long videos and propose a novel framework FrameThinker. Within this framework, LVLMs are able to iteratively interrogate video content. 
Developing such video reasoning capabilities in LVLMs presents notable challenges, particularly in adapting the model to new video actions (e.g. select frame), and designing reward functions to guide LVLMs to adopt the newly introduced action. 
To solve these challenges, 
we propose a two-phase training strategy, first employing Supervised Fine-Tuning (SFT) to instill fundamental action capabilities, followed by Reinforcement Learning (RL) to optimize a strategic decision-making policy.
Notably, in this RL phase, we conduct an in-depth and comprehensive exploration of the reward design for each action and format reward. 
Extensive experiments on reasoning benchmarks like Video-Holmes, LongVideo-Reason, and long-video understanding benchmarks such as LongVideoBench, MLVU, VideoMME, and LVBench, demonstrate that FrameThinker gets a significant average improvement of +10.4\% over baselines while drastically reducing the number of processed frames. 
Most notably, our 7B model, FrameThinker establishes a new state-of-the-art on LongVideo-Reason, achieving 76.1\% accuracy using an average of only 20.6 frames. This not only outperforms the competitive LongVILA-R1 (72.0\%) but does so with over 20x fewer frames (vs. 512), demonstrating unparalleled efficiency and effectiveness. 
Our code is available at:
\url{https://github.com/lcqysl/FrameThinker}.

Published as a conference paper at ICLR 2026
FRAMETHINKER: LEARNING TO THINK WITH LONG VIDEOS VIA MULTI-TURN FRAME SPOTLIGHTING
Zefeng Hel2
Xiaoye Qu
Yafu Li
Siyuan Huang*
Daizong Liu
Yu Cheng
SShnngaai AI Laboratory
2Nanjing University
'The Chinese University of Hong Kong
4 *Shanghai Jiao Tong University
'Peking University
ABSTRACT
While Large Vision-Language Models (LVLMs) have achieved substantial progress in video understanding, their application long video reasoning is hin- dered by uniform frame sampling and static textual reasoning, which are ineffi cient and struggle to handle visually intensive video tasks. To overcome these challenges, in this paper, we introduce the concept of thinking with long videos and propose novel framework FrameThinker. Within this framework, LVLMs are able to iteratively interrogate video content. Developing such video reason- ing capabilities in LVLMs presents notable challenges. particularly in adapting the model to new video actions (e.g. select frame), and designing reward func- tions to guide LVV.Ms to adopt the newly introduced action. To solve these challenges, propose two-phase training strategy, first employing Supervised Fine-Tuning (SFT) to instill fundamental action capabilities, followed by Rein- forcement Learning (RL) to optimize a strategic decision-making policy. No- tably, in this RL phase, we conduct an in-depth and comprehensive exploration the reward design for each action and format reward. Extensive experiments reasoning benchmarks like Video-Holmes, Long Long-iideoRRaaon, and long-video understanding benchmarks such Long VongBBdB,,,,ch, MLVU, VideoMME, and LVBench, demonstrate that FramkThinker gets a sigmificant average improve- ment of +10.4% over baselines while drastically reducing the number of pro- cessed frames. Most notably, our 7B model, Frame'Thine establishes new state-of-the-an on LongVideo-Rens Reaso-, achieving 76. accuracy using an aver- age of only 20.6 frames. This not only outperforms the competitive LongVILA- RI (72.0%) but does so with over 20x fewer frames (vs. 512), demonstrating unparalleled efficiency and effectiveness. Our code is available at: hhtpp: how.ithubccmm/in www.c/ooo/ccccccccccccccc.. www.eriaaeeeeeeee.....eee
INTRODUCTION
In recent years, the remarkable advancements of Large Vision-Language Models (LVLMs) have been extended into the challenging domain of video understanding. State-of-the-an closed-source models such Gemini-2.5 (Comanici et al. 2025). GPT-4o (OpenAl 2024) and GPT-5 (Ope- nAl 2025a), alongside powerful open-source counterparts like Qwen2.5- VL. (Bai et al. 2025) have established performance benchmarks
suite of video understanding benchmarks, such as Video-MME (uu et al. 2025a) and LVBench (Wang et al. 2024a).
However, a fundamental limitation still persists the operational paradigm of existing methods (Bai et al. 2025 Chen et al. 2024c; 2025b). This limitation lies in their reliance on processing a large, pre-selected set of frames obtained through uniform sampling, as illustrated in the top panel of Fig- ure I This strategy suffers from inefficiency, especially for long video reasoning, as it processes a large number of irrelevant frames. Meanwhile, such a long and noisy context, cluttered with irrel- evant frames, degrades the reasoning performance (Qu et al., 2025a). Furthermore, these methods conduct their reasoning process exclusively through text tokens, which restricts their multimodal perceptual abilities after the initial input. Concurrently, while Video Agents (Fan et al.) [2024] Wang et al. 2024b 2025 Zhang et al. 2025b) can interact with videos using tools, they often depend on
Corresponding authors.

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Published as a conference paper at ICLR 2026
predefined workflows or external models, which limits their autonomy and flexibility in dynami- cally exploring video content based on reasoning needs. Furthermore, most of these methods are not end-to-end learnable from data, which restricts their potential for improvement.
To address these limitations, we introduce Frame a novel framework that empow- ers the model to perform an active and itera- tive analysis of the video content. As clearly illustrated in the bottom panel of Figure [1 our model first conducts sparse scan (Turn 1)
Selected Frames
Unscleeted Frames
Key Frames
Turn
to establish a broad and general understand- ing- Guided by this initial assessment, then identifies promising temporal segments and ex- ecutes a targeted "zoom-in" by selecting spe- cific sequence of frames (Turn 2) to retrieve more fine-grained and detailed visual informa- tion. This iterative refinement process can be repeatedly applied as needed, fully guided by the model's own reasoning, thereby allowing the model to dynamically gather the necessary evidence while processing only a fraction of the total frames. In this way, our FrameTThinke Thinker constructs a multimodal chain of thought that interleaves textual reasoning with visual frames, ultimately empowering the model to "think with long videos".
Choose E' Framms
Turn
Figure I: (Top) Traditional Uniform Sparse Sam- pling is inefficient and may miss the key frame in long videos. (Bottom) Our method starts with sparse scan for an overview (Tur 1), then dy- namically chooses frames on promising segments (Turn 2), enabling a multi-turn analysis effi ciently focus on key frames in a long video.
Our training methodology facilitates this capability in two stages: we first employ Supervised Fine- Tuning (SFT) to acquaint the model with the core mechanics of action execution (e.g. select frame), followed by Reinforcement Learning (RL) to cultivate an optimal policy for strategic action. Fur- thermore, we conduct an extensive exploration of the reward function design space for multi-turn video analysis. Among our key findings, we identify that unconditional action rewards are highly prone to mode collapse. While shifting to a simple conditional reward structure mitigates this issue, it does not prevent the model from learning to output illogical actions. To address this deeper chal- lenge, we further propose a Cognitive Consistency Verification (CCV) module to suppress illogical executions and ensure the rationality and interpretability of the model's actions.
To validate the effectiveness of Frame Thinker, we evaluate it on complex reasoning benchmarks like Video-Holmes, (Cheng al. 2025) and Long Video-Reason (Chen et al.) (025b), as well on long- video comprehension benchmarks including Long VideoBench (Wu et al. (2024b), MIVU (Zhou et al. 2024), VideoMME (Fu et al., 2025a), and LVBench (Wang al., 2024a). Across all tested benchmarks, our model consistently achieves superior results, outperforming the baseline by an average of 10.4% in accuracy while using significantly fewer frames. Notably, on the Long Video- Reason benchmark, Frame'Thine Thinker achieves an accuracy of 76. % using merely 20.6 frames on aver- age, surpassing the competitive LongVILA more frames (512), and establishing new state-of-the-ane Furthermore, on the reasoning-intensive Video-Holner benchmark, our method achieves an accuracy of 46.8%, which also establishes a new state-of-the-art while using remarkably fewer frames.
(Chen et al., 2025b) (72.0%) which requires 20 times
To summarize, our main contributions are threefold:
for long video reasoning, novel framework that empowers LVLMs to actively and dynamically focus on reasoning-intensive video frames, shifting the paradigm from passive video processing to active, multi-turn iterative reasoning.
We introduce
We conduct a comprehensive investigation of the reward design space for multi-turn video reasoning. Our deep dive into reward conditioning reveals that even conditional rewards can reinforce illogical thought-action pairs. This motivated our proposal of the Cognitive Consistency Verification (CCV) module to enforce logical consistency and interpretability.
We demonstrate through extensive experiments that FrameThinker achieves superior ac- curacy while using significantly fewer frames than previous methods, establishing new state-of-the--rt performance challenging reasoning benchmarks.
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2 RELATED WORK
LVL.Ss have significantly advanced video understanding (Liu et al. 2021 2024a bj 2020 Qu et al. 2020; Fang et al.) 2026: 2024b a). Following the release of DeepSeek- R1 (Guo et al.
Video Understanding.
2025), a new wave of models based on Reinforcement Learning with Verifiable Reward (RLVR), including Video-R1 (Feng et al.] 2025). , VideoChat-RI (Li et al. 2025), and
LongVILA-R RI (Chen et al.] (025b), has demonstrated substantial performance gains, even sur- passing proprietary models certain benchmarks (Chen al., 2025b). However, these methods predoninantly rely on a passive, uniform frame sampling strategy, which limits their efficiency and effectiveness on long videos. In this paper, challenge this paradigm by introducing Frame- Thinker, a framework that empowers the model to actively and iteratively interrogate the video.
Video Agents. Video agents (Wang et al. 2024b Fan al. 2024 Wang et al.) 2025 Zhang al., 20256 Liu et al., 2025) typically rely on external tools to perform complex tasks. With improve- ments both these tools and LVLMs, video agents now achieve strong performance on multiple benchmarks. Despite their success, most methods rely on fixed workflows, with decision policies that cannot be learned from data, which limits their flexibility. autonomous, end-to-end policy directly from data, enabling the model to decide when and how to interact with the video while leveraging its reasoning to guide visual exploration.
contrast, our approach learns an
Thinking with Image. Traditional LVLMs (Bai et al. 2025 Chen et al. 2024c; 2025b) typically process visual information as a static starting condition, after which all subsequent reasoning unfolds purely in the textual domain. Recently, the emerging paradigm of "Thinking with Images" (Su et al. 2025a b/c OpenAl 2025b; Zheng et al. 2025) has challenged this approach by enabling models to iteratively consult and leverage visual data improved performance. Inspired by this evolution, we propose Thinker,, which empowers the model dynamically query video for relevant frames based on its evolving cognitive state, thereby enabling the model to "think with long videos".
integral part of their thought process, leading to
3 METHOD
3.1 FRAMEWORK OVERVIEW
As illustrated in Figure 2 instead of processing the video in single, passive pass, our Fram- eTThinkr framework engages in a multi-turn reasoning loop, actively and strategically seek- ing information from the video for its decision-making, At each step, the model first gener- ates a textual thought and then selects a specific action from a predefined action space. This explicit separation of reasoning and action is articulated through structured output format: <think>... </think><actti ion>............ ion.... </act ion>. Formally, for a given input query q, the model produces sequence of thought-actionnoosseraiin triplets, namely trajectory т:
T ((t1, o1,,,),..... (ln, an.nn),)))
(1)
where for a given query q at step j. ty represents the textual thought (i.e <think> tag) generated by the model. This component captures the model's explicit reasoning for its subsequent action. aj the action (i.e <action> tag) selected by the model. oy the observation returned by the environment after executing action aj. For instance, 0j could be set of frames, frame number, or a terminal signal. n the total number of steps in the trajectory T.
More complex and diverse examples of the model's reasoning process are provided in Appendix D In this paper, we explore the below three actions for long vidco reasoning:
choose frames between START_FRAME and END_FRAME This is the primary action for visual exploration. It allows the model to retrieve a sequence of frames (c.g.. frames) from specific temporal segment of the video, enabling a "zoom-in" capability.
get frame number at time MM: This auxiliary action enhances the model's temporal awareness. It translates human-readable timestamp into precise frame index, which can then be used in subsequent choose frames actions.
output answer: This is terminal action that concludes the reasoning process. The model executes this action when it has gathered sufficient evidence to confidently provide the final answer the user's query.
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(a) Triner Framework
(b) Training Pipeline
User Query: Who helped the little sandpiper overeome its fear of the waves?
1. Data Preparation
VLM
SFT Data
Frame
Frame 3017
Frame 3149
Frame 7331
<think?... Frame 3149 shows the bird looking fluffy and terrified after being hit by a wave, while Frame 7331 shows confidently FrameThinky and happity submerged in the sand us a wave passes over ... thinkk>
II. SFT
Frome Thinker
F7.35
caction=choose frames between 149 and 733 </action>
Opimicatoon
III. RL
1
Action Roward
Ascuriyy Reward
Rolloot
Advantage
Frame 5502
Frame 5715
Frame 6027
Frame 6441
<think.... The new frames show the sandpiper ... hermit The evidence is now sufficient to conclude... <ithink>
Cognitive Consislancy Verilication (CCV)
TumeThinker crahs
1. Redurdancy Check
Logical Flow Check
<gction-outour answer: The hermit crab </action>
Fidelity Check
Figure 2: (a) illustration of the iterative reasoning process of our proposed FrameThiner. The model first performs sparse sean, then uses thought- steps to progressively gather evidence. The CCV module ensures this process is logically consistent and interpretable. (b) Our three-stage training pipeline, consisting of Data Preparation, Supervised Tine-Tuning (SFT) to learn action syn- tax, and Reinforcement Learning (RI.) to optimize the policy.
3.2
SUPERVISED FINE-TUNING
To equip FrameThinker model with the rect syntax for the defined actions, we first con- duct Supervised Fine-Tuning (SFT) phase. This phase utilizes a small, curated dataset of only 2.392 examples, designed specifically to instill the basic action grammar. The composi- tion of this data illustrated in the left panel of Figure 3 with detailed breakdown provided in Appendix G The more complex task learn- ing strategic policy is deferred to the subse- quent Reinforcement Learning (RL) phase.
12.7
11.7%
8,5%
55.0%
Dunsthttion
thrn
NExT-0A
With this curated SFT dataset. fine-tune the model using standard autoregressive language modeling objective. The model is trained to predict the next token the ground-truth trajec- tories, which composed of both <think> and <act ion> sequences. Specifically, we compute a standard cross-entropy loss, but only on the tokens generated by the model (i.e., the content within the <think> and <act inn> tags). The input query and the observations from action execution are treated as context and are excluded from the loss calculation. This phase ingrains the fundamental mechanies of action execution, preparing the model for the more strategic learning in the subsequent RL phase.
STAR
Figure 3: The distribution of data sources for the SFT (Left) and RL training phases (Right).
3.3 REINFORCEMENT LEARNING (RL)
While the SFT phase successfully equips the model with the basic syntax of action execution, its su- pervised nature has inherent limitations. Reliance small, fixed dataset can create tendency for the model to memorize specific solution paths, rather than truly generalizing the underlying reason- ing strategy (Chu al. 2025 Zhang al.) 2025a Qu et al. 2025b Yan et al.) 2025). This approach leads to fragile policies that suffer from limited generalization when faced with novel scenarios. To transition the model from memorization
generalization, introduce the Reinforcement

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Learning (RL) phase. Crucially, this phase utilizes much larger and more diverse dataset of 28k examples, which discourages overfitting to specific patterns and compels the model to learn a more robust, generalizable policy. The composition of this dataset is illustrated in the right panel of Fig-
ure with further details available Appendix G
We adopt Group Relative Policy Optimization (GRPO) (Shao al. 2024 Guo al. 2025 to
optimize the policy during the RL phase. For each query 9, we sample G trajectories {T; i=1 from the old policy Tea and normalize their outcome rewards construct the advantages. We omit the KL divergence term to improve efficiency and avoid prematurely constraining the policy's 's search for optimum. (Yu al. 2025). The objective is defined as:
1
(O) E Mer
min (ryAs, clip(r;, 1 1,,,,-1,,14 A.)
(2)
G 1=1
( (g)
A R² mean (f {R1, Rgl)
where
TV Tea (T: ④)
std( [R], Ra)) +
Here, R; is the reward for trajectory Ti, is the clipping hyperparameter, and á is a small constant for numerical stability.
Given that the interactive, multi-turn nature of our framework introduces unique context for video understanding, raises new questions for reinforcement learning, particularly regarding how to design an effective reward function. Therefore, in this paper, we make the first attempt compre- hensively explore the design space of the reward function detail the following paragraphs.
3.3.1 SHOULD WE USE FORMAT REWARD?
Reinforcement Learning with Verifiable Reward (RLVR) frameworks (Guo al. 2025) often in- clude format reward. We initially explored this reward but observed a critical issue: the model quickly learned to suppress action usage, discouraging exploration, as shown in our ablation study (Section 4.3). The cause is that large format reward (e.g., comparable to accuracy reward) creates perverse incentive, Early training, the model discovers that outputting a final answer without rea- soning, even a random guess, provides low-risk way to stably earn the format reward. Attempting actions, in contrast, risks a format error and a zero reward.
Based on this finding, deliberately omit the format reward. This choice is further justified by our framework's execution engine, which inherently penalizes malformed actions: an invalid ac- tion triggers execution error, prematurely terminating the trajectory and forfeiting any chance of receiving the final accuracy reward.
3.3.2 SHOULD ACTION REWARDS BE UNCONDITIONAL. CONDITIONAL?
We initially experimented with an unconditional action bonus, where the model receives a reward simply for executing action, regardless of the final outcome. However, we observed that this approach often leads model collapse. For instance, the model might learn to repeatedly call the same simple action in loop its reasoning collapsing into meaningless, repetitive text. A detailed analysis of these failure modes provided in Appendix E.1
attempt to execute multiple actions within single turn, with
Therefore, we shifted to a conditional action bonus, where rewards are granted only when an action part of trajectory that leads to correct final answer (Ru 1). Our total reward is formulated as sum of this outcome-basse accuracy reward and a process-based action bonus:
Riotal Race
Raction
where the conditional action bonus, Ruction: is defined as weighted sum over the actions taken:
Raction >et 1 E T + >xtn l(gfn T)
(5)
where der and Aptn are the reward weights for choose frames and get frame number.
While this conditional reward structure resolves the issue mode collapse, we found that a more subtle challenge still remains: the model might learn execute spurious illogical actions that,
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by chance, are part of successful trajectory. For instance, the model might select different frame interval in its action than what it decided in the thought process, or it might request the frame number for specific timestamp but then choose frames from completely different interval (examples can be seen in Appendix [E.).. This highlights the need for mechanism to enforce consistency between the model's reasoning and its subsequent actions.
To prevent the reinforcement of such flawed reasoning paths, we introduce the Cognitive Consis- teney Verification (CCV) module. During training, this rule-based module acts as a filter after roll- out. It is applied to every trajectory т to validate its logical consistency by checking for redundancy, logical flow, and fidelity between thought and action (detailed Appendix [E.3). Any trajectory that fails these checks is immediately terminated. The final reward, Runal, is then calculated as:
Rainll
Piona VCCv(T)
where (TCv(T) is a verification function that returns if the trajectory T passes the Cognitive Con- sistency Verification, and 0 otherwise. This mechanism effectively suppresses the model's 's attempts to cheat for rewards by assigning a zero reward to illogical trajectories. During the inference phase, the CCV module serves runtime safeguard. If illogical action is detected, the current at- tempt is terminated, allowing for retry or a fallback. The CCV module ensures that the generated chain of thought serves a verifiable trace of the decision- process, which is crucial for interpretability. It also yields additional performance gains, as detailed in Section 4.3
3.3.3 WHICH ACTION SHOULD THE REWARD PRIORITIZE?
With the reward structure defined, the next design question is how to set the weights, Aar and Agtn- to encourage the most effective behavior. We deliberately set Agtn >> Aet because supervising the choose frames action is difficult; even if an action is logically consistent and part of success- ful trajectory, it does not guarantee the action itself was meaningful. The model could have retrieved irrelevant frames and still reached the correct answer using information from the initial query previous turns. In contrast, for time-specific tasks, the get frame number action is objectively correct and highly informative, providing precise, verifiable information that directly supports sub- sequent decision- decision-making. This, in turn, provides indirect supervision for the choose frames action, as the bonus is only awarded if the model subsequently uses the obtained frame number correctly within its selected interval, a condition enforced by the CCV module.
3.3.4 SHOULD WE ENCOURAGE THE MODEL TO PERFORM AS MANY TURNS AS POSSIBLE?
In our multi-turn interactive setting, natural question arises: should we explicitly encourage the model to take more turns? To investigate this, we replace the previous action reward with a newly designed function that directly promotes additional turns:
Raction k -T-
where 7 is the total number of turns the trajectory and a small positive constant. The subtrac- tion of accounts for the final action to output answer, which is not an analysis step.
We conducted experiments with both unconditional and conditional rewards and observed that, al- though the model's 's average number of turns initially increased, training quickly became unstable and subsequently collapsed, with the model's reasoning degrading into meaningless statements.
(Detailed training curves and case studies are provided in Appendix F..2) We conclude that this reward setup makes the training unstable: the model, in its pursuit of higher cumulative rewards, learns to prioritize increasing the number of turns over maintaining coherent reasoning and achiev- ing the actual task objective. Therefore, we did not adopt this configuration in our final design.
4 EXPERIMENTS
Our empirical evaluation is conducted in two main stages: small-scale and large-scale experiments. Both stages share an identical Supervised Fine- Fine-Tuning (SFT) phase, but they differ in the Rein- forcement Learning (RL. phase. Due resource constraints, the small-scale experiments serve as

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Table 1: Performance on Vidco-Holmes. The table breaks down the overall accuracy into seven
sub-tasks: Social Reasoning (SR). Intention & Motive Chaining (IMC), Temporal Causal Inference (TCI), Timeline Analysis (TA), Multimodal Hint Reasoning (MHR). Physical Anomaly Reasoning (PAR), and Core Theme Inference (CTI). *denotes model trained on Video-Holmes.
Model
Frames
SR
IMC
TCI
MHR
PAR
CTI
Overall
Closed Source Models
GPT-4o OpenAA
2024]
32
50.0
49.6
38.8
30.0
44.0
39.2
37.0
42.0
Gemini- Pr-PPr cam et al 2023
52.1
48.2
34.4
26.0
39.2
46.4
38.9
41.2
Gemini-2.5-PP Comanici et al
2025
46.6
49.3
46.9
53.0
40.1
44.3
37.4
45.0
Open Source Models
GRPO-CARE Chen et
2025g
42.8
35.1
25.6
40.5
29.2
29.9
32.6
33.5
GRPO-CARE" Chen 2025a
46.2
44.9
31.5
49.5
39.2
37.1
37.4
40.7
Video-R1 [Feng
2025
32
48.6
41.7
28.9
34.5
31.0
33.5
35.9
36.5
VideoChat-KI [[. 2125
32
42.
38.8
24.5
39.5
29.5
27.8
29.3
33.0
Qwen2.5-VL-7B [aa at
2025
32
38.4
34.8
17.6
30,0
27.1
18.6
25.2
27.8
(Ours)
10.2
58.9
58.7
49.1
60.0
56.0
53.6
49.1
56.1
primary platform for conducting detailed ablation studies. These studies crucial for vali- dating our experimental setup and examining key aspects of the training methodology. such as the design of the reward function. Specifically, for the small-scale experiments, we perform RL training exclusively on the training set of Video-Holmes (Cheng et al.) (025). We choose this benchmark because its emphasis active clue-seeking for reasoning aligns perfectly with the philosophy of our framework. Conversely, the large-scale experiments are focused on training our final model on our complete RL dataset to achieve the best possible performance and enhance ability to general- ize across diverse problem distributions. The resulting model is then used to report our final results against competing methods on these benchmarks, validating the effectiveness of our approach.
4.1 BENCHMARKS AND BASELINES
We evaluate our method on comprehensive suite of six benchmarks: Video-Holmes (Cheng al. 2025 Long Video-RRRRaoo (Chen et al., 2025b), Long VideoBench (Wu et al. 2024b), MLVU Zhou al. 2024), VideoMMMB-Long w/o sub) (Fu al. 2025a), and LVBench (Wang et al. 2024a). These benchmarks are strategically chosen to assess distinct capabilities. The first two benchmarks, and LongVideo Video-Reason, are designed to evaluate the model's advanced reasoning abilities. The subsequent four focus on long-video comprehension across a spectrum of increas- ing durations, among which VideoMMMB--Long and LVBench specifically challenge performance in exceptionally long scenarios. Further details on each benchmark are provided in Appendix G.]
Our baseline models are chosen to provide a comprehensive comparison against leading models. We select Qwen2.5-VL-7B 5-VLBB (Bai et al. 2025) as our primary baseline for direct comparison, and we also include Video-R1 (Feng et al.) (025) and VideoChat-RI (Li et al. 2025), as they are also based on Qwen2.5-VEL VL-BBB. Furthermore, for broader context, we also report the performance of other models as cited on the official benchmark leaderboards.
4.2 IMPLEMENTATION DETAILS
model. In Reinforcement Learning (RL), the number of frames sampled each turn was adaptively set: 8 frames for most videos, and 12 frames for longer videos (> 300 better handle the extended temporal context. The primary reward is based on the final accuracy (Race = 1), supplemented by action reward, with no format reward. The small-scale experiments used learning rate of 1.0e-6 and reward bonuses Agta 0.2, Aer = 0, while the large-scale training used a learning rate of 5.0e-7 and reward bonuses Agtn = 0.5,
Our implementation is based on the -L-e2.5-VLL-BB--
Aer 0.02. Further implementation details provided in Section B

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(a) Ablation the effect (b) Ablation on the effect (c) Ablation on the effect (d) Ablation the effect of training stages.
of format rewards.
of the CCV module.
of reward configurations.
Figure 4: Comprehensive ablation studies on key components of our training methodology. (a) Direct comparison against a fine-tuned Qwen2.5- VLL-BB baseline. (b) Impact of including format reward. (c) Ablation on the CCV module during training and inference. (d) Performance under different action reward configurations.
4.3 SMALL-SCALE EXPERIMENTS
Main Results. When trained on Video-Holmes, our model achieves an accuracy of 56. 1%, estab- lishing a new state-of-the-art, as shown in Table Our method not only surpasses existing models in accuracy but also use significantly fewer frames on average.
Ablation Studies. We conducted series ablation studies to quantify the advantages of our framework, with the results visualized in Figure
First, we conducted a direct comparison by fine-tuning the baseline Qwen2.5-VL-77B model using our identical SFT or RL setups. The results, presented in TableHa demonstrate that our full approach achieves a clear performance advantage, confirming the efficacy of FrameThinker's architecture.
Next, present the impact of format reward. As shown in Figure 4b the version trained with format reward exhibits rapid decline in actions during early stages of training, indicating that such reward can inadvertently suppress the model's 's incentive to explore and utilize actions.
Furthermore, we validated the effectiveness of our Cognitive Consistency Verification (CCV) mod- ule. The results in Figure 4c show its impact when applied during the training and inference phases, underscoring that the CCV module's ' contribution extends beyond just enhancing interpretability; also leads to tangible improvement in performance.
Finally, we investigated the impact of different configurations for action reward, with the results presented in Figure 4d Our findings indicate that assigning larger reward bonus to the get frame number action than to the choose frames action (in this case, Agin = 0.2 and Aer 0) yields the most significant performance benefit. We attribute this to the accurate and reliable information provided by the get frame number action.
4.4 LARGE-SCALE EXPERIMENTS
Table 2: Performance reasoning benchmarks. tindicates results evaluated by us. *deotees model trained on Video-Holmes.
In large-scale experiments, conducted a comprehensive evaluation of FrameThinker across six challenging benchmarks, demon- strating its consistent superiority in both ac- curacy and frame efficiency. The model's strength is particularly evident on benchmarks designed to test complex reasoning abilities (Table2). For instance, on Long Video-Reason, our model establishes a new state-of-the-art re- sult. It achieves 76.1% accuracy using merely 20.6 frames on average, surpassing the strong LongVILA-RI baseline which requires 512 frames.
Model
Video-Holmes
LongVideo Reason
Frame
Frame
Cloned Modely
OpenAl
Gemini- 1.5
Team
2023
32
41.2
69.3
Open Sonne Models
LongVILA Chan et al
20246
62.7
LoggVII A-T--
207 Sh
$12
72.0
GRFO-CARE
33.5
ERPO-CARE-
Video-RI Fone
32.
36.5
VideoChat RI
La di 2073
32
33.0
32
67.21
Qwen255VVL VL---VLL-D 7L
ct 2015
32
27.8
64.11
FrameThinker
+19.0
This trend of high performance and exceptional frame efficiency continues across the four long-video comprehension benchmarks (Table When
viewed holistically, FrameThinker well-rounded capabilities become clear. The radar chart in Fig-
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Published as a conference paper at ICLR 2026
Video Hotmes
(8
Longt Vednn Seccon
56
55
Bonah
Meethault
53.2
GPT-4)
10
Oas thaand dtacta tinaaa Vaios
Avenge Notenn
50.2
Ac0000y
85
#5.
RotaMtte Lang
15
+2
30
25
Vido-III
Qwen2 5-V
Nanterr
ViloClus-I
FruneTirke:
(a) Performance Vidco- Holme..
(b) Overall performance.
(c) Average accuracy comparison.
Figure 5: (a) Accuracy and the number of frames processed on Video-Holmes. (b) A radar chart comparing overall performance across six benchmarks, where results are normalized and scaled for visual comparison. (c) Average accuracy across six benchmarks. Our TrameThhnker achieves the best average performance.
Table 3: Performance on long-vided benchmarks. findicates results evaluated by us.
Model
Beng
MLVU
Video.MME-LLng
LVBench
Frame
Acc
Frame
Acc
Frame
Acc
Frame
Acc
Closed Source Madels
GPT-4o OOpenAl
2024
$8.5
0.5lps
64.6
384
65.3
48.9
Gemini- 1.S-Pro
2023
32
55.2
0.5fps
67.4
3600
33.1
Open Source Models
PLLaVA (u et al.
2024
16
40.2
16
26.1
ShareGPT4Viddeo Chen
2024b
16
39.7
46.4
16
35.0
Long Zhang al. 2024
56.3
128
VITA-1.5- /B [Iu
2025b
128
46.2
Video-RI (Feng et al.
2025
32
52,7f
60.21
32
48.21
35.31
VidenChat-KI [La
2025
32
49.
54.31
32
46.21
34,31
Qwen2.5-VL-7B (Bai ct al.
2025
32
43.2
32
48.41
32
41,91
32
31.6"
FrameThinker (Ours)
21.1
52,9
23.2
59.1
24.1
47.6
23.9
36.6
-34%
+9.7
-28%
+10.7
-25%
+5.7
-25%
+5.0
ure 5b provides visual summary, illustrating its advantage over competing methods in various benchmarks. Quantitatively, this superiority is best captured by its average performance, as shown Figure [cc] Thinkrh achieves an average accuracy of 53.2%, representing a significant im- (Qwen2.5-VL-77) and its derivatives were evaluated using Chain-of- Chain-of-Thought prompting strategy, with the specific prompt detailed in Ap- pendix C33 For detailed training curves, Appendix
provement of +10.4% over the baseline Qwen2.5-VL-
4.5 COMPUTATIONAL EFFICIENCY ANALYSIS
While FrameThinker demonstrates unparal- leled efficiency in terms of the number of frames processed per task, we further evalu- ate its real-world performance by analyzing the Wall-Clock Latency (Time). The results are summarized in Table
Table 4: Computational Efficiency and Wall- Clock Latency on Video-Veesssse Inference was conducted with HuggingFace Transformers.
Model
Frame
Acc
Time (s)
Video-R1
32
68.1
8.6
The results in Table clearly validates that Frame frame-efficient, active reason- ing strategy translates directly into superior real-world computational speed. FrameThinker achieves new state-of-the-ae th-t-e-rrtt art accuracy of 76.1 1% on Long maintaining extremely low average latency of just 7.7 seconds per task.
VILA-RI
256
70.8
38.1
LongVILA
512
72.0
0OM
Framerhinker
20.6
76.1
7.7
while simultancously

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Published as a conference paper at ICLR 2026
5 CONCLUSION
In this paper, we introduced FrameThinker, a novel framework that advances video understanding by shifting the paradigm from passive, uniform sampling to active, iterative analysis. Our model is trained to dynamically interrogate video content, intelligently selecting and reasoning over mini- mal number of frames to find the answer. Our training pipeline consists of Supervised Fine-Tunine Tunin (SFT) and Reinforcement Learning (RL). Within this framework, we further explore the reward de- sign space for multi-turn video analysis, and introduce a Cognitive Consistency Verification (CCV) module to ensure that the model's s actions remain logically grounded, interpretable, and aligned with its reasoning. Our extensive experiments show that FrameThinker achieves state-of-the-art perfor- mance on challenging reasoning benchmarks while drastically reducing the required visual context. This work demonstrates significant advance in video understanding, creating powerful models that can achieve more comprehensive reasoning with drastically less visual context.
ETHICS STATEMENT
Our research is grounded in the use of publicly available, established academic benchmarks for video understanding. We have not collected any new data containing personal or sensitive informa- tion. The primary goal of our work is to advance the efficiency and reasoning capabilittes of LVLMs, which we believe is a positive contribution to the research community. However, we acknowledge that like any powerful video analysis technology, Frame could potentially be misused for ap- plications such as large-scale surveillance or automated content filtering, which could have negative societal impacts. Our intention is strictly for research purposes, and did not design the frame- work for such applications. By open-sourcing our code and models, we aim to foster transparency and encourage further research into both the capabilities and the potential societal implications of such models, allowing the community to collaboratively develop appropriate safeguards.
REPRODUCIBILITY STATEMENT
To ensure the reproducibility of our results, will make our code and model checkpoints pub- licly available upon publication. The core code is currently provided for review via an anonymous link. Our work implemented based on the open-source Diwe22.5vV--7B-Instrrc model. All datasets used for the Supervised Fine-Tuning (SFT), Reinforcement Learning (RL), and final eval- uation phases are publicly available academic benchmarks. We provide a detailed breakdown of these datasets in Appendix G Our key experimental configurations are thoroughly described in the Implementation Details (Section 4.2). Furthermore, the specific prompts used for our model and the baseline evaluations are provided in Appendix C We believe these resources provide sufficient detail for the community to verify our findings and build upon our work.
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APPENDIX
APPENDIX CONTENTS
Use LLMS
14
More Implementation Details
14
Prompts
15
C.1
System Prompt for FrameThhinkr
15
Fallback Prompt for CCV Failures
15
Chain-of-Thoughh Prompt for Baselines
15
Additional Examples
15
Bad Case
20
Mode Collapse from Unconditional Rewards
20
2
Mode Collapse from Multi- Rewards
21
Trajectories Failing Cognitive Consistency Verification (CCV)
23
Premature Stopping
26
Training Dynamics
27
Datasets and Benchmarks
28
Details of Datasets and Benchmarks Used
28
G.2 Details of SFT Data
29
A
USE OF LLMS
During the preparation this manuscript, we utilized L.M to aid in the writing process. The LLM'' role was limited polishing the text and improving its readability. Specifically, was used for proofreading to identify and correct grammatical errors, spelling mistakes, and awkward phrasing. The authors reviewed and edited all LLM-generated suggestions ensure accuracy and take full responsibility for the final content of the paper.
B MORE IMPLEMENTATION DETAILS
Additional Training Details. In the original setup, Supervised Fine-Tuning (SFT) was performed using parameter-efficient LoRA (Hu et al. 2022) with rank of 8 on 8 H800 GPUs. A per-device batch size of with 4 gradient accumulation steps was used, resulting in an effective batch size of 32. The initial learning rate was set to 1.0c-4, and cosine learning rate scheduler with warmup ratio of 0.1 was applied. Reinforcement Learning (RL) training was conducted on H800 GPUs with batch size of 32 and rollout number of 8.
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C PROMPTS
C.I
SYSTEM PROMPT FOR FRAMETHINKER
The detailed system prompt used to guide FhankThinker during experiments is shown in Fig- ure 6 This prompt defines its available actions, and the required format for its thought and action process.
You are an expert Al assistant that answers questions about video by iteratively unalyzing it. Your task is to output your reasoning within a <think> </think> tag, followed by specific action within an <action> </action> tag.
Possible actions are:
1. choose frumes between START FRAME and END FRAME': Request a more detailed view of specific video
segment. The number of frames is fixed, currently [CONFIG[ FRAMES TO SAMPLE")),
2. get frame number at time MM:SS Get the exact frame number for specific time, Convert hours to minutes if needed
(egg, for hour, minutes, and seconds, use 62:30).
3. uuipul unswer: OPTION: : Provide the final answer (v.g., A, B, C...) when you are confidemt.
Figure 6: The system prompt for FrameThinker. It outlines its task, the specific syntax for action usage (get frame number and choose frames), and the structured <think>. ... <action>> output format.
C.2 FALLBACK PROMPT FOR CCV FAILURES
During the inference phase, the CCV module acts as a runtime safeguard. it detects an illogical action (i.e., failure in the Logical Flow Fidelity Check), the ongoing iterative process is termi- nated. To prevent the model from getting stuck or failing the entire task, fallback mechanism is triggered. The model is then presented with new, simplified system prompt, shown in Figure 7 This prompt instructs the model to provide a direct, final answer directly, which ensures a robust response.
You expert assistant that questions about vidco. Your task is output your reasoning within <think> </think> tag, followed by speeific action within <action> </action> tag. Your only action is: "oupput
OPTION' Provide the final answer (e.g., A, C...).
The fallback system prompt triggered upon a CCV failure during inference. directs the model to formulate final answer directly.
Figure 7:
C.3 CHAIN-OF-THOUGGIT PROMPT FOR BASELINES
Additionally, we provide the Chain-of- Chain-of-Thought (CoT) prompt used for evaluating baseline models, such Qwen2.5-VL-7B and its derivatives, illustrated in Figure To ensure fair comparison, adopted the same evaluation prompt as used in the original Video-Holmes benchmark (Cheng al., 2025).
Based on the given video, reuson and answer the single-choive question. Provide reasoning between the <think> and </think> tags, and then give your final answer between the <answer* and <lanswer> The question is: {question). The options are: (options). Your answer:
Figure 8: The Chain-of-Though prompt used for evaluating baseline models.
D ADDITIONAL EXAMPLES
To further illustrate the capabilities of our framework, we present additional qualitative examples. In the following cases, only small subset of the frames retrieved and processed is visualized.
Figure9demmnn demonstrates how the model handles precise, ceesstampsppccife query by first using an auxiliary action locate the event.
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disuppearance of wall reflection when pressing switch imply? A. Switch control gas valve
Question:
B. There is malfunction in the electrical circuit inside the house.
The switch is in the off position when it turned upwards.
D. The angle specular reflection changes
E. Someone cut off the power supply.
F. Produce the phenomenon of visual persistence
Frame
Frame 849
The user is usking ubout an event that happems at specific timestamp, 0:13. To undersland what this event implies, first need to locate the exact frame al this hattauppp//hhinnn<acciiieee frame number at time 00: 1/</cction>>
<think>
Frame number at time 00:13 is: 311.
<think The frame number is 311, which falls between the initially provided frames and 849. Observing the frames between frame and frame 849 should provide the necessary htta.://ttiii--aatioooooooss frames between and 849</action>
wach?
SWITCH
<think> The set of frames confirms that the switch is being turned and off. The reflection the wall disappears when switch in position. This directly supports option C. The disappearance of reflection is clear indicator of the switch's state. I have enough information to make a decision. rithink-Saction>en
answer: Cs/action>
Figure 9: An example of our framework solving a reasoning task. Given a question about a specific timestamp (0:13), the model first formulates thought and executes the get frame number action. Based on the returned frame number, initiates a second reasoning turn, using the choose frames action "zoom in" on the relevant scene. This multi-tum, iterative process allows the model to gather the necessary visual evidence (the state of the light switch's 's reflection) to arrive at the correct answer.
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Figure 10 highlights the model's ability to solve ambiguous, non-timestamp-speciie questions through its iterative exploration strategy.
Question: What relationship between the man black the man red?
A. brother
B. Hunter and Fugitive
C. friend
D. Doctors and patients
E. colleague
F. stranger
Frame 2259
Frame 3766
<think> The user wants to understand the relationship between the two men. The initial frames show man in green jacket and another in red hoodie. The scene transitions from peaceful confrontation the woods, which suggests conflict. The most dramatic and revealing moment seems physical struggle is depicted. To understand their relationship better, need to examine the sequence of events more closely between the initial meeting and the fight. I'll focus my search on the segment between frame 2259 and frame 3766. </think S <action>choose frames between 2259 and 3766</action>
around frame 3766, where a
<thinle-The new set of frames confirms my hypothesis. The man in black is shown the aggressor, chasing eventually attacking the man red. This dynamic is best described as \"Hunter and Fugitivel". The man red is clearly fleeing and being pursued. The evidence now overwhelming. <thinkk><ctionnoouppp answer: B</action>
An example of resolving an ambiguous query without specific timestamp. Given only sparse initial frames that suggest a conflict, the model's first thought to hypothesize crucial temporal segment between the peaceful beginning and the depicted struggle. It then executes choose frames action to "zoom in" on this interval. The newly retrieved, denser frames provide unequivocal evidence of chase and attack, allowing the model confidently deduce the "Hunter and Fugitive" relationship and select the correct answer. This case demonstrates the effectiveness of the model's iterative refinement process for complex narrative understanding.
Figure 10:
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Figure 1 presents more complex reasoning trajectory, showcasing the model's ability to perform a hierarchical, multi-step search.
Question: What the girl use find the person she looking the video?
Mask
B. Tail
Phone
Car
Frame 16407
Frame 32814
<think> The user wants to identify the tool used by the girl to find someone. Looking at the initial frames, we see two children (frame 16407) and then large creature (frame 32814). The question likely pertains event that happens between these two points. To find the object, need examine the segment more closely. focus my search between frames 16407 32814. </thinkk-<cciionzccoo frames between 16407 3281 4<<action>>
Frame 23438
Frame 25782
Frame 28125
<think>The new set frames clearly shows woman a hood (frame 23438) giving a mask to the girl (frame 25782, 28125). This action seems to be directly related to the girl finding the person she is looking for. have seen the mask being given and then being worn. The key event is between frames 25782 and 28125. need to examine this part closely confirm that this mask used find the person. will focus my search between frames 25782 28125. <tthinko-<cction>do frames between 25782 28125</action>
<think-The new frames confirm that the girl wears the mask and uses it to navigate through the forest, finding the way to the final battle. The mask is clearly shown being given to her and then being worn by her. have gathered enough in information. The correct option is A, Mask. I am confident in my wttp::///hhiin-cccioooooppeo answer: A</action>
example multi-step reasoning process that showcases hierarchical search. The model begins with broad search (choose frames between 16407 and 32814) 4) based on the sparse initill frames. After this first action reveals the mask as a key object, the model formu- lates a new thought and performs a second, more targeted search (choose frames between 25782 and 28125) to confirm its function. This iterative refinement strategy, moving from coarse to a fine-graines analysis, demonstrates the model's advanced capability to solve complex problems by efficiently focusing on the most relevant visual evidence.
Figure 11:
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demonstrates where the reasoning process reconsiders the initial analysis and per- forms targeted search on a completely new temporal segment.
Figure 12
Question: How did the
prepare and spread ingredients the dough before adding toppings? Spread chopped onions and sprinkled pepper on the dough.
B. Spread butter and garlic on the dough before aciinng herbs.
C. Layered temato slices und grated cheese on the dough.
Arranged potato slices and chopped onions the dough, then sprinkled salt.
Frame
Frame 6117
Frame 36702
Frame 42819
<think>... Frame 42819 shows toppings being added. Frame 36702 shows the dough being prepared. It's preparation
logical that the ingredient spreading step mentioned the question
between the dough
sauce application. Therefore, should investigate segment between frame 36702
frame 42819 to see how the ingredients are applied to the dough before the sauce. I will choose frames between 36702 and 42819. 42819.</thinks <athinn""<ction>> caction>choose frumes between 36702 and 42819<42 9///ctoti
<think>My previous analysis led section This did align with any the options provided. ... Let me reconsider the initial set frames. Frame particularly interesting. shows large, rectangular piece dough covered with layer of what looks like sliced potatoes. A person is also seen slicing potatocs on the counter. This scene is quite different from the later frames showing the preparation of round pizzas. It's plausible that the question refers this specific preparation the beginning of the video, which I may have initially overlonked. T'ee question asks ingredients were prepared and spread before toppings. The process in frame 0 seems to be exactly that. To verify this, I reed to see what happens immediately after frame 0. ... will choose frames between 0 and 6117. wtt.s/ttttscctooooaaa saction>choose frames between and 7/17///t
Frame 874
Frame 1748
Frame 4368
<think-Thh The analysis of the frames from the beginning the video has provided clear sequence of actions. The initial frames show the person slicing potatoes and arranging them on a restangular dough. Subsequent frames show the preparation and spreading of chopped onions (or similar vegetable like locks) on another section of the dough (frames 874 and 1748). Frame 4368 clearly depicts the person sprinkling salt from bowl the prepared dough. This sequence of arranging potato sliees and chopped onions, followed by sprinkling salt, directly corresponds to option Therefore, have sufficient evidence confidently seleet the correct answer w/thickt////ttttttee Sactioo--tttiituuupuu answer: D</action>>
Figure 12: An example of complex reasoning trajectory featuring Reconsideration and Multi- interval Exploration. Based on sparse initial scan, the process initially focuses on a segment between Frame 36702 and 42819 19 (identified as dough and sauce preparation). After the first targeted search fails to provide a matching option, the trajectory identifies a key visual difference in Frame (showing rectangular potato-covered dough) compared later frames (showing round pizzas). The reasoning then reconsiders the initial segment and shifts focus to new, completely different temporal segment (choose frames between 0 and 6117), thereby showcasing a flexible multi-interval exploration strategy. The new set of frames provides the necessary visual sequence (arranging potato slices, chopped onions, and sprinkling salt), which leads to a confident selection of the correct answer (D). This case highlights the robustness of the policy in self-correction.
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In contrast to the multi-turn examples that require exploration, Figure optimal efficiency where the model performs a "Direct Answer."
13
Question: What appears on the sereen at the very beginning of the video?
A. The lake
B. The mountain with green frees
C. The prairic
D. The snow mountain
Frame 0
<think>The user is asking what appears the sereen the very beginning of the video. The initial frame provided, fiame clearly shows suow-covered mouutain. This directly corresponds to option The subsequent trames show different scenes, but the beginning is unambiguously the mountain. am contident in this answer based on the first frame alone. <thlnk>><cctio>>>o
answer: D</action>>
An example of Direct Answering. The user asks about the very beginning the video. The model correctly identifies that the provided Frame 0 already contains the necessary information. In its thought process, it recognizes the sufficiency of the initial evidence and confidently proceeds directly to the output answer action without invoking any actions. This case demonstrates the model's s strategic capability to not only explore when necessary but also to conclude efficiently when the answer is readily available, avoiding redundant actions.
Figure 13:
E
BAD CASE
E. MODE COLLAPSE FROM UNCONDITIONAL REWARDS
In our initial explorations of reward design for reinforcement learning, we observed that improper re- ward mechanisms could lead the model to learn invalid, repetitive strategies phenomenon ponnommmon known as mode collapse. This issue was particularly pronounced when providing unconditional rewards for specific actions, as the model would learn to exploit the reward function rather than solve the task.
Unconditional reward for the get frame number action. We first experimented with a reward function where fixed, positive bonus, specifically set to 0.2, was granted if the model executed the get frame number action at least once within a trajectory, regardless of the final answer's 'ss cor- rectness. This unconditional incentive led to severe mode collapse, illustrated in Figure [4] The model's behavior degraded in two significant ways. First, its reasoning process became incoherent and circular; the content within the <thi nk> tags devolved into nonsensical, repetitive phrases like "I need to first" without any substantive analysis of the vidco content. Second, the model learned to repeatedly execute the exact same action (get frame number at time 00: :2) without any intention of using the resulting information, demonstrating a clear failure to engage in meaningful, gral-oriented exploration.
The underlying cause of this collapse is that the model discovered a simple, low-effort strategy to maximize reward. The unconditional action bonus provided high-certainty, immediate reward, whereas the reward for correct final answer (Ra.r) was uncertain and required complex, multi- step reasoning. Consequently, the model learned degenerate policy: ignore the task and simply perform the rewarded action to get the bonus. This strategy effectively circumvents the intended learning objective, discouraging any genuine attempt to understand the video and solve the problem. This critical observation underscored the necessity of making action rewards conditional on the final task success, principle we adopted in our final reward design.
Unconditional reward for the choose frames action. Similarly, observed a distinct but related form of mode collapse when providing an unconditional reward of 0.2 solely for the choose frames action. In this scenario, the model is rewarded for executing this action, again, without any
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Question: What does scene balloons bursting one by one at 0:22 imply?
Atmospheric pressure changes
Birthday magic eflect.
Electrical short-cireuit sparks
D. The crowd sabolaged in secret.
E. Invisible creatures move
F. Time loop sign
<think-Thhe question asks about specific event described in the question stem. First, I need to verify the premise of the question.
first need to confirm the question's premise.
reed to first defermine the question's context.
The question asks about "balloons bursting". get context this question,
need find information about this
need first establish if this question asking about plot.
need to first understand the question.
need to find the question's question. will first investigate the question's question <a fiame number at time 00:22</action>
Frame number time 00:22
<think-After getting the frame number and cheosing the question, I need to understand the question's question. Fisst, I need to assess the question's premise.
The question likely refers to a horror movie plot. need analyze the question to get context. comtext-=tthlnl--aan -ithinl--cccctoonn-e frame mumber time 00:22-c/cctoon>
Framo number time 00:22 527.
<think>Aftte getting the frame number, I need to understand the question's guesticn.<</hllll--aaae frame nunber at time 00:22-/hctlon>>
Frame number time is: $27.
<think>-Atter getting the frame number, need investigate the question. I need to unulyze the question </think-cactionnoonn answer: Es/action>
Figure 14: An example of mode collapse induced by unconditional reward for the get frame number action.
dependency on the final outcome. As shown in Figure 15 the model's response degenerates into single, elongated turn. Within this turn, the model's reasoning is again replaced by nonsensical, tautological statements about "options and choices." Crucially, proceeds to execute the choose frames action multiple times in quick succession. These actions are often repetitive or illogical, lacking any strategic motivation derived from coherent thought process.
The cause of this behavior is analogous to the previous case. The model identifies that repeatedly outputting the rewarded action is an efficient strategy for accumulating rewards. Instead engaging in multi-turn dialogue to incrementally gather evidence, it learns to "stuff" single turn with as many rewarded actions as possible before concluding with a likely random guess. This instance of mode collapse further reinforces conclusion that unconditional rewards fundamentally flawed for training a strategic model. They incentivize the model to master the exploitation of the reward function itself, rather than learn the complex, causal reasoning required solve the actual task. This led us to develop a conditional reward system, which we detail the main paper.
E.2 MODE COLLAPSE FROM MULTI-TURN REWARDS
Reward for More Turns. Also, investigated alternative reward designed encourage more extensive reasoning. This was implemented using the step-based reward function, shown Equa- tion 7 For this experiment, set the reward for each turn to 0.2 (corresponding to k 0.2 in the
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Question: What is most likely relationship between Emma the denttst?
A. accomplice
B. husband and wife
C. doctor-patient
D. Stranger
E. relatives
colleague
<think>TTh user asking about options and choices likely appear in options or choices' descriptions or choices' options list. <ithink><acttonnnge get frame number at time =300s/cctoo=<<think: The user is asking about choices or options related to choices or options about choices or options about
frames between 1768 and 1856=/action>><thnn -thnnk---iiiss action describes options choices that likely discuss oplions or choices about choices' options or choices' options.</thinggg
options. options.</thinkes cactiie---atin->c-haa
framee between 1768 and 1856=/action
I'is action explains the frames berween 1768 and
choices available the options list. option discusses choices and options related options about choices options. Wihinneeaciiionnhhose frumes between 1768 und 2656<vac thnn uplion explains the context and choices presented in the thoices.</thinke "iliinn*<ctionn choose frames between 1768 and
1:56=//cttoonn<thinb
2656</action- aeatonn-thiiii -hink> This action explains the choices and options for this specific question's question
options antionss</thinlee wthink--acctionehhoeee frames between 1768 and thi This option explains the options ur chuices' options their options. "
answer ex: C</nction>>
Figure 15: An example of mode collapse induced by unconditional reward for the choose frames action.
equation), with the total bonus capped at 0.6. We explored both an unconditional and conditional version of this reward.
First, in the unconditional setting where the bonus is granted regardless of the final outcome, the training process proved highly unstable, as illustrated in the top row of Figure T6 Initially, the strategy appears successful: the average number of interaction turns per trajectory steadily increases, peaking at over three. However, around step 270, the training abruptly collapses, with the average number of turns plummeting to a degenerate state. This policy collapse is mirrored in the average response length, which simultaneously destabilizes and drops. As shown in Figure 17 the model's thought process is entirely eliminated, with the <think> tag simply mirroring the <act iictiio ion>. It then terminates the process in the subsequent turn with random answer, completely failing the task.
In addition, we investigated conditional version, where the bonus granted only upon a correct final answer. However, we observed a similar pattern of training instability, as shown in the bottom row of Figure [6] Although the policy collapse occurred at a different point in training (around step 170), the overall dynamic was similar: the average number of turns initially rose and then catastrophically dropped. This demonstrates that directly incentivizing the model to take more turns, even when the reward is conditional, remains an unstable objective that leads to policy collapse.
The reason for this collapse rooted the inherent instability multi-tur turn reinforcement learning, which this reward design drastically exacerbates. Training policy for long-horizon, multi-turn in- teractions is intrinsically challenging due to issues like high variance and difficult credit assignment. The model must learn to maintain a coherent reasoning state across many steps. The reward function in Equation 7 however, creates a perverse incentive that steers the model away from this difficult task. As the graphs vividly show, the model learns that the path of least resistance is not to solve the problem, but to prioritize extending the trajectory. It abandons the complex goal of coherent rea- soning in favor of a simpler policy: generate just enough valid syntax to prompt the next turn. This leads to the observed collapse, forcefully demonstrating that the quality and purpose interactions, rather than their sheer quantity, what must be rewarded.
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133
Reguone loy!
181
Boegune Lereth
A00002
Step
Soog
100
Respeese Langt
71
3300
Reggune anghh
A65010
Figure 16: Training dynamics. (Top Row) Metrics from training with an unconditional reward. (Bottom Row) Metrics from training with conditional reward. The final output answer action not included the action counts.
Question: What hint of girl's phone note shown in 0:15 shot?
A. The other party's number is encrypted.
B. Dialing the emergency call.
C. The remark shows "Danger".
D. The contact person familiar with object.
E. The phone battery about to run out.
F. The address book is empty.
<think>choose frames between 0 and woooe/thinkk-ccctonnnccc frames between 0 and 6000</action>
<think<-output answer: De/thinle-saction-ouupp answer: D</action>
Figure 17: An example of mode collapse induced by reward that unconditionally encourages higher number turns.
E.3 TRAJECTORIES FAILING COGNITIVE CONSISTENCY VERIFICATION (CCV)
The Cognitive Consistency Verification (CCV) module is designed to filter out and penalize tra- jectories that exhibit illogical or inconsistent reasoning patterns. This ensures that the model is rewarded for coherent reasoning rather than for finding loopholes in the reward system, and en- hances the model's s interpretability. Below, we detail the three primary failure conditions that the module checks for.
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Failure to Pass the Redundancy Check. First, posit that coherent reasoning process should be progressive and avoid redundancy. Therefore, the CCV module checks if the model executes actions with identical content. Specifically, it flags any instance where action with the exact same parameters executed more than once within a single trajectory. For example, repeatedly calling get frame number at time 00:22 get the same frame number, repeatedly execut- ing choose frames between 100 and 200 to retrieve the same set of frames, would be penalized. Such repetitions indicate that the model is either stuck in a logical loop is not effec- tively using the information from its actions to advance its reasoning. We observe that this type of redundancy primarily present during the early stages of training or in cases of mode collapse, as exemplified in Figure 14 where the model repeatedly requests the same timestamp. Such behavior becomes rare once the training process stabilizes.
Failure to Pass the Logical Flow Check. Second, the CCV module enforces a logical sequence for actions that are causally linked. This rule primarily addresses how the model utilizes informa- tion from the get frame number action. When this action retrieves specific frame number, subsequent choose frames action is expected to make meaningful use of this new information. A failure to do so is flagged logical inconsistency, as it demonstrates the model is not properly connecting its reasoning steps.
An example of this logical breakdown is presented in Figure 18 The model correctly identifies the need to investigate timestamp (0: 33) and successfully executes get. frame number, which returns frame 815. This is critical piece of information obtained through explicit action. How- ever, in its very next step, the model requests frames from the interval 565 - 6 645, This interval does contain frame 8 815, rendering the previous action useless and indicating disjointed reasoning process. The CCV module identifies this failure to utilize causally linked information and terminates the trajectory.
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Question: What was Jeremy's intention in supporting the back of the bundle 0:34?
Prevent items from slipping off.
B. Adjust the bundling ungle
C. Relieve lower back pain
D. Cover the outline of the human body
Check the tightness of bundle
F. Soothe the emotions of the bound.
<think> The user is asking about an event that happens very early in the video, at 0:34, First, I need to find the exact frame number to determine the context around this specific action. aetion.</thnkke <athiink--sctttonngg frame number time 0:34</action>>
Frame number at time 0:34 is: 815.
tthink>The user''s question about action that likely question "supporting the back of bundle at 0:34" is probably located around frame 815. options related to this specific event are likely located in the frames around this frame number. The action described in the question probably located in the initial frames IVve been provide.-</thnnee </thinnk=sctooncchoos frames between 565 and 645</action>
frame 815. The action mentioned in the
<think-Upon examining first frame and action grabbing hand, it's clear that the "bandle"
mentioned in the question is not a physical object but a person. The action al frame 815 (0:34) likelly refers to a person being tied up. The action described in the question is probably located in frame 632. The action about supporting the back is not physical action related preventing slipping (ruling A). There'ss evidence adjusting the angle (ruling out B). There''s no mention of pain (ruling out C). ThereV's no evidence for checking tightness (ruling out E). Option F seems plausible as it's general action in the context. The term "bundle" is used metaphorically. The action related to this event is likely described in frame 610-620, The context around this action probably located in frame 610, which at frame 815 in the question. </think> ouuui answer: D</action>
Figure 18: An example of Logical Flow Check failure. The model first successfully retrieves frame 815 corresponding to specific timestamp. However, its subsequent choose frames action targets an interval (565-645) that does not contain the retrieved frame, breaking the logical chain of actions.
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Failure to Pass the Fidelity Check. Finally, the CCV module ensures that the model's executed action faithful to its preceding thought process. The module parses numerical arguments from both the <think> tag and the subsequent <action> tag to check for contradictions. This check crucial for preventing the model from learning to generate plausible-sounding reasoning that is detached from its actual behavior, thereby suppressing the reinforcement of flawed or "hallucinated" reasoning paths.
Figure 19 presents clear example of trajectory that fails this fidelity check. In the <think> block, the model's reasoning identifies that the key event is located near frame 4974. How- ever, the executed action, choose frames between 1400 and 1500, completely disre- gards this analysis and targets an entirely unrelated video segment. By identifying this logical in- consistency, the CCV module terminates the trajectory and assigns a zero reward, preventing such flawed reasoning-action pairs from being positively reinforced.
Question: What docs the cheesy flying saucer design at the end imply?
A. Human dependence dairy products
B. The carth's environment polluted.
C. The girl's hallucination is visualized.
D. Government secret experiment device
E. Deformation of spacc debris
Alien creatures' trapping strategics
GIASS CAMMON
<think-Tho The user is asking about an event that likely happens towards the end of the video. The question mentions "cheesy flying design," which is probably located near frame 4974, The term "cheesy" often used describe plot hole a silly explanation movie. The action must be located near frame 4974,</think Sattinik-ccctionocoosss frames between 1400 and 1500</action>
<think=Upon closer inspaction frame 1499, it''s clear the frames show supernatural horror element rather than a literal "cheesy" explanation. This suggests the option about the "cheesy flying saucer design" is likely near frame 1499. This frame range shows supernatural theme. The question about the "cheesy" design''s implication must be located in this range. The term "cheesy" in this context probably used to describe plot point. This specific
located frame ptt.ittinn---cctoozoouuup
C</action>>
An example of a Fidelity Check failure. The model's reasoning in the <think> block identifies the relevant area around frame 4974, but the executed <action> targets an entirely unrelated interval (1400-1500). The CCV module flags this trajectory as inconsistent.
Figure 19:
E.4 PREMATURE STOPPING
In this section, we present an example of FrameThinker limitation: Premature Stopping. This failure mode is often characterized by missed events due to poor initial scans or an overly aggressive attempt to conclude the reasoning process. The case illustrated in Figure 20
For extremely long videos, sparse sampled frames are inherently limited and may not provide suf- ficient information to pinpoint the exact location of the answer. As shown the example, the model correctly analyzes the query and identifies the beginning of the video the relevant area for the "opening caption" " and correctly executes the choose frames action on the initial segment (frames 0 to 7997). However, visual information (the target year) in the newly sampled frames. The model prematurely gives up on further visual exploration. Instead, attempts make unmature inference based solely
with this targeted scan, the model still fails to secure the explicit
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the incomplete historical context of the quote it has found. This speculative deduction leads to incorrect final answer (A) and highlights challenge in policy learning where the model prioritizes early termination over comprehensive evidence gathering.
Question: What year uppears the opening caption the video?
A. 1636
B. 1366
1363
1633
Frame
Frame 7997
<think>T The user usking for the year shown in the opening caption of the video. Based on the initial frames, the video starts frame 0 and the year is likely present in this initial segment. To find the specific yeur, I need closer look at beginning the video. will examine frames between frame clearer picture of the opening credits or cuption. Therefore, will choose to observe the frames between frume and frame 7997. satiil---action>cooooo frames between and 7997</action>
frame 7997 get a
<think~After reviewing the frames from 0 to 7997. can see a text card with quote. While the year isn't explicitly written on this card, it's of the opening sequence. The options are 1636. 1366, 1363, and 1633, The historical context of the quote suggests period in Japan's history. The year 1636 is famous for the Edo period's strict isolationist policies, which aligns with the quote "Citizens would never see white face, nor any face that was not Japanese." The other years are too carly for this specific historical context. Based on this evidenec, the opening caption refers to the year 1636. am now confident in this conclusion. <ithink=-acctonn-oupp answer: A A</action>
An example of a Premature Stopping failure. The model selects the correct temporal segment but fails to find the target year, opting instead to guess based on an incomplete, historical inference. The correct answer is D.
Figure 20:
F TRAINING DYNAMICS
The dynamics of the large-scale Reinforcement Learning (RL) training phase are illustrated in Fig- ure 21] These metrics provide comprehensive overview of how the model's policy and behavior evolve as it learns to interact with the video environment to solve tasks.
As shown in Figure 21a the primary objective-average average accuracy-ehhibit a consistent upward trend, demonstrating that the policy is successfully learning. The learning curve begins to converge around step 1100 as the model's performance on the task distribution approaches its peak.
The average action reward (Figure 21b displays more complex, two-phase pattern. rises initially and then stabilizes, before resuming a significant upward trajectory after approximately 850 steps, point which corresponds to roughly one full epoch over the training data. We interpret this as the model shifting its optimization focus as it begins its second pass through the dataset. In first epoch (up to step 850), the model learns the fundamental use of actions to solve the more straightforward cases, establishing a solid baseline accuracy. Once this primary accuracy reward begins to plateau, the policy must refine its strategy to rewards more challenging problems. As it re-encounters the data the second epoch, it learns to employ actions with greater precision and skill. This mastery of more nuanced action use necessary solve these harder instances,
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resulting in higher average action reward and allowing the model to continue increasing its total reward even as accuracy gains become marginal.
Figure 21c reveals the evolution of the model's action utilization strategy. The training begins with a noticeable drop in the average number of actions. We attribute this to the model entering the RL phase with incomplete mastery of the action space, it trained on only small SFT dataset and had not yet fully learned the correct format and usage actions. The RL process quickly penalizes erroneous superfluous actions, leading this sharp corrective decline. Subsequently, as the model accurately learns the utility of beneficial actions, their usage rate recovers and fluctuates before settling into stable range, indicating convergence
more purposeful and efficient policy.
Finally, the average response length (Figure 21d shows a general downward trend. Our SFT data synthesized using Gemini-2.5-pro (Comanici et al. 2025), a powerful, closed-source model that tends to generate longer, more detailed chains of thought, especially in such multi-turn scenarios. The Qwen2.5-VL. VL--BB base model initially mimics this verbose style. However, during the RL phase, without explicit reward for length, the policy naturally gravitates towards shorter, more concise outputs. This reflects an optimization towards efficiency, where the model retains the core reasoning necessary for task success while shedding the stylistic verbosity of the teacher model, behavior more aligned with its own inherent capabilities.
Cligueal Actice Anrped
Seeweled
Acion MauanRouut Rouand
A000000
0310
(a) Average Accuracy
(b) Average Action Reward
Roppree Langhh
300
-30
215
3.00
30.0
1300
220
1900
100
Stop
Step
(c) Average Actions per Trajectory
(d) Average Response Length
Figure 21: Key metrics from the Reinforcement Learning training phase. (a) The model's accuracy on tasks steadily improves. (b) The reward attributed to taking actions increases, showing more effective action use. (c) The number of actions (excluding the output answer) per task stabilizes after an initial exploration phase. (d) The model's generated response (thought + action) becomes more concise over time.
G DATASETS AND BENCHMARKS
G.1 DETAILS DATASETS AND BENCHMARKS USED
Video-Holmen Cheng al. 2025 features short suspense films with rich plots and implicit cues. Its core characteristic is requiring the model to act like a detective, actively searching for and con- necting multiple scattered visual and audio clues across different video segments. This process
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facilitates deep reasoning about causality, motivation, and themes, far exceeding the simple identi- fication of isolated clues found in traditional benchmarks. We incorporate its training set into our training data and evaluate our final model on the official test set.
Long Video-Reason (Chen et al. 2025b) is designed to comprchensively enhance and evaluate the deep reasoning capabilities of vision-language models on long videos. It includes question- answering content across various complex reasoning types, such as temporal, object-related, spatial, and plot-based inquiries. Similarly, we utilize its training set for our training process and conduct our evaluation on the test set.
LSDBench (Qu al. 2025a) is characterized by its high "Necessary Sampling Density" (NSD) questions. These questions specifically target very brief yet action-dense segments within long videos (e.g., one hour). This design rigorously tests a model's ability to locate and parse critical information amidst backdrop of redundant data.
CG-Bench (Chen et 2024a) is a novel, clue-driven question-answerte benchmark for long video understanding, Its main feature is the emphasis on requiring the model to answer questions based on specific clues provided within the video context.
NEXT-QA (Xiao et al. 2021) pushes video understanding from simple action description to deep action explanation. It focuses on reasoning about the causal relationships (why something happened) and temporal relationships (what happened before/after an event) between actions in videos.
STAR (Wu et al. 2024a) is benchmark designed to evaluate a machine's capability for situated reasoning in real-world videos. A key feature is its abstraction of complex dynamic scenes into structured hypergraph representations. programmatically generates questions centered on four reasoning types-interaene interrrtctttoonn sequence, prediction, and feasibility- to conduct in-depth diagno- sis of a model's visual perception, contextual understanding. and logical reasoning skills.
Perception'Test (Patraucean et al. 2023) is novel multimodal video benchmark created to diagnos- tically evaluate a model's perceptual and reasoning abilities. The dataset contains videos specifically recorded to showcase interesting perceptual scenarios.
Long VideoBench (Wu et al. 2024b) is a novel benchmark specifically designed to evaluate the long-context video understanding capabilities of Large Multimodal Models (LMMs). Its core feature is the inclusion of a diverse range of videos with durations of up to one hour.
MLVU (Zhou et al. 2024) is benchmark for evaluating the long video understanding capabilities of Multimodal Large Language Models (MLLMs). It is distinguished by its significant extension of video lengths, ranging from 3 minutes 2 hours, and its inclusion of diverse video genres such as movies, surveillance footage, and gameplay.
Video.MME (Fu et al., 2025a) the first comprehensive evaluation benchmark for Multimodal Large Language Models in video analysis. Its key characteristic is its all-encompassing design, which covers wide variety of video types (6 domains, 30 sub-categories) and broad temporal span, including short, medium, and long videos ranging from 11 seconds to 1 hour.
LVBench (Wagg et al., 20244) is characterized by its extremely long video durations, averaging over one hour, which significantly surpasses previous benchmarks of its kind.
We primarily evaluate our method on a comprehensive suite of six benchmarks: MongViddo-Reaaee Long VideoBecc,,, MLVU, VideoMMMF-Long (w/o sub), and LVBench. As il- lustrated in Figure 22] these benchmarks are strategically chosen not only to assess distinct capa- bilities but also to cover progressively increasing range of video durations. The first two, Video- Holmes and Long Video-Reason, are specifically selected to evaluate the model's advanced reason- ing abilities on complex narratives. Among them, VideoMMEE-Lngg and LVBench specifically test the model on exceptionally long video scenarios, evaluating the robustness of our approach.
G.2 DETAILS OF SET DATA
The Supervised Fine-Tuning (SFT) dataset consists of two subsets, as detailed below. Process-Supervisse Data. This subset is constructed from five reasoning templates:
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70
Video Holmes
LongVideo Reason
60
Long LongVideoBench
MI.U
Average Length (min)
50
VidcoMMEE-Long
LVBench
40
30
20
10
Adeo-Holmes
Vidgnn Renson RennVicenRench
MLV::
VideMMFF-Long
IVBench
Benchmark
Figure 22: Average video length (in minutes) across the six primary evaluation benchmarks.
A. Direct Answering: Answers the query directly without any action.
B. Single-Pass Frame Selection: Performs single choose f frames action before answer- ing.
C. Linestamp-Grounder Selection: Uses get frame number for timestamp, then choose frames to inspect the relevant segment.
D. Hierarchical Frame Selection: Performs a second choose frames action that refines previously selected interval.
E.
Exploration: Performs choose frames on two distinct intervals.
Outeome-Supervised Data. For this subset, supervision applied only to the correctness of the final answer, not the reasoning path. The distribution of these reasoning templates, alongside the number of actions per trajectory, illustrated in Figure 23
15.5%
25.5%
16.9%
23.4%
3.8%
The trajectories for both subsets synthesized using Gemini-2.5-Pra (Comanici et al.) 2025). To ensure high-quality dataset, first retain only trajectories that lead to the correct answer and filter out instances with illogical reasoning paths using Gemini-2.5-Flashh (Comanici et al. 2025). This curation process ensures that the SFT phase focuses on learning meaningful and effective action execution strategies.
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Figure 23: Distribution of reasoning templates (Left) and actions per trajectory (Right).
30


Reviews:
[{"review_id": "cO9YNOfK15", "reviewer": "Reviewer_jZYr", "summary": "The paper introduces a novel framework, FrameThinker, that reframes long video understanding. Other than using a traditional single-pass processing, FrameThinker uses multi-turn frame reasoning process, which dynamically highlights the relevant frames. By generating textual thoughts hint guided by actions prior, it focus on promising segments by choosing specific frame ranges.\n\nIn specific, it didn’t load large set of frames but learn to engages in a multi-turn reasoning loop. Then it utilizes the dynamic actions strategies which the primary action is chosen with the start and end frame within the “highlighted” range, allowing to extract the critical information based on its reasoning. After that, they uses a two-phase pipeline for training: 1. Using a supervised fine-tuning tiny dataset as teaching model for the syntax and mechanics of actions; 2. Using a larger dataset with reinforcement learning to train a strategic policy on controlling the timing to use these actions for. They provides a comprehensive reward design correspondingly for this task in order to point out unconditional rewards that lead to collapse and set conditional action bonus only on final success. In addition, they introduce a cognitive consistency verification (CCV) module to suppose illogical executions and ensure the interpretability of the model’s actions.\n\nThe author demonstrates the performance of FrameThinker by conducting extensive experiments to gain superior accuracy while using significantly fewer frames.", "questions": "•\tWould the CCV flag a valid but complex reasoning path as an error, and how do you plan to scale these rules to more complex reasoning?\n\n•\tDuring the SFT phase (Phrase 1 training), if the teacher failed to give a basic action syntax for the features of SFT, it might likely to significantly decrease the performance on the final performance since it mainly relies on the quality of the teacher model’s data.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "•\tThe paper tackles a critical challenge in long video understanding: the efficiency and interpretability of long video due to uniform frame sampling for long-term reasoning. The motivation is interesting, and the core idea of “multi-turn frame spotlighting”. The author successfully builds up a pipeline of combining thoughts and actions along with observations. It mimics human-like analysis (skimming, then focusing) and moves the field from passive processing to active, agentic reasoning.\n\n•\tCognitive Consistency Verification (CCV): The paper carefully addresses the reward collapse issue during training with an unconditional action bonus. The CCV module is a novel and effective build-up component that acts as a filter after the rollout process in order to validate every trajectory by checking possible redundancy, logical flow, and fidelity. CCV ensures the generated chain of thoughts during the decision-making process. \n\n•\tSOTA on extensive experiments: With fewer input frames as final feed, delta and performance are consistent. FrameThinker reaches SOTA performance on xis benchmarks(Video-Holmes, LongVideo-Reason, LongVideoBench, MLVU, VideoMME-Long, LVBench).", "weaknesses": "•\tAction Space Flexibility: The paper mentioned that the current action space will depend on the selected range. It can help pinpoint the action to make predictions more accurate, but will also limit the generalizability of the model if it fails to get enough action within the range.\n\n•\tCCV’s robustness: Since CCV is a rule-based module, it weakens its generalizability when facing a more complex, interactive scenario, which creates a more difficult reasoning path. CCV might lose its advantage in complex scenes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "styg8PCa4v", "reviewer": "Reviewer_gjov", "summary": "This paper proposes a LVLM that actively reasons over long videos by alternating thoughts and actions in multiple turns, trained via SFT then RL with a rule-based Cognitive Consistency Verification (CCV) filter to keep thoughts and actions logically aligned. The SFT process is trained to learn observe-think-action process, and the RL is to learn what are the frames and when in the timestamp to sample. The integrated CCV is to surpass the illogical thought-action pairs. Results on LongVideo-Reason, Video-Holmes demonstrate the method's frame efficiency. However, there are some drawbacks such as reward shaping is fragile (unconditional or naive bonuses can cause collapse) and CCV is rule-based rather than learned, so behavior may hinge on handcrafted checks and hyperparameters", "questions": "1. The authors measure efficiency mainly as \"frames processed per question.\" How does FrameThinker compare to LongVILA-R1 / Video-R1 in terms of actual FLOPs or wall-clock latency, given you do multiple LVLM passes per example?\n2. Why not compare against a baseline that uses uniform frames but also multi-turn CoT (repeated LVLM calls) to match your compute pattern more fairly?\n3. How would FrameThinker adapt to long-video tasks where the supervision is not QA-style (e.g., temporal localization, dense captioning, or video editing assistance) where rewards and clean correctness signals are harder?\n4. How many examples does the model answer correctly without really using new frames (i.e., from the question or initial sparse scan alone)? Do you detect significant \"shortcut\" behavior?\n5. How robust are results to the exact CCV rules and thresholds? If you relax or slightly perturb them, does performance drop sharply?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Prior LVLMs, Qwen2.5-VL, LongVILA-R1, mostly do one-shot reasoning on a big uniformly sampled frame set. FrameThinker instead actively selects frames over multiple turns, so it hits higher accuracy with far fewer frames.\n2. Compared to Video-R1 and VideoChat-R1, which are also built upon Qwen2.5-VL-7B, FrameThinker consistently scores higher on long-video benchmarks while using similar or fewer frames, showing that the active frame reasoning policy actually adds value beyond the backbone. \n3. Video agents, e.g., VideoAgent, RIVET-like systems, typically rely on manually designed workflows or external tools and are not trained end-to-end; they follow a pre-defined pipeline for querying frames or detectors. While this method makes frame operations part of the LVLM’s own action space and trains them with RL, so the policy for \"where to look next\" is learned from data, not hard-coded. That’s a big step up in autonomy and adaptability vs those agent-style baselines.\n4. Compared to many video agents, FrameThinker adds Cognitive Consistency Verification (CCV) that checks redundancy and fidelity of thought-action pairs, so the proposed method can more easily spot and filter illogical exploration instead of trusting a black-box planner.", "weaknesses": "1. The paper’s efficiency claims are measured only in terms of the number of frames processed before feeding to the fixed LVLM, which is not an end-to-end computation. Compared to prior RLVR and CoT baselines that perform a single-shot pass on a uniformly sampled frame set, FrameThinker introduces extra multi-turn reasoning with repeated LVLM calls over successively updated contexts. While this is likely more frame-efficient, it is unclear whether it is actually more computationally efficient overall. \n2. The method is not compared against any SOTA that also performs reasoning-based or active frame sampling on long videos, making it difficult to justify the benefits of the proposed design.\n3. Can the method be plug-and-play module before any LVLMs? The proposed FrameThinker requires an action grammar, a bespoke RL setup, a rule-based CCV module, and nontrivial reward tuning.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eylE2to55G", "reviewer": "Reviewer_wput", "summary": "This paper addresses long video reasoning by introducing FrameThinker, a framework that enables a vision-language model to iteratively select and analyze frames across multiple reasoning turns. The model begins with a coarse video scan and selectively retrieves frames for closer inspection based on its evolving reasoning. It is trained in two stages: supervised fine-tuning to learn tool syntax, followed by reinforcement learning to optimize its frame selection policy. Key contributions include new action primitives, a multi-turn reasoning paradigm, and carefully designed reward mechanisms for stable training. The method achieves state-of-the-art accuracy on video QA benchmarks while using far fewer frames—for instance, 76.1% on LongVideo-Reason with ~20 frames versus 72.0% from prior work with 512. Overall, FrameThinker delivers significant accuracy gains (+10.4% on average) with substantially higher efficiency.", "questions": "- Frame Selection Ablation - Have you considered comparing FrameThinker to heuristic or learned frame selection strategies beyond uniform sampling? Including such baselines would help clarify how much of the performance gain comes from the learned policy versus the general benefits of dynamic frame selection.\n- Speedup - What is the computational speedup from processing fewer frames? The paper shows a drastic reduction in frames (e.g., 20 vs 512), but due to the multi-turn approach, there may be multiple forward passes. How does the actual inference time or FLOPs compare to a single-pass baseline? Some discussion or measurement of runtime efficiency would strengthen the claim of “unparalleled efficiency.”\n- Generality - How adaptable is FrameThinker beyond QA tasks? Could it handle video captioning or anomaly detection without an explicit query, or is a well-defined question essential for guiding frame selection? Exploring this would strengthen the paper.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Novelty - The paper introduces a new multi-turn “thinking” paradigm for video understanding, which is notable in this context, although not a fundamentally new algorithm. By allowing the model to iteratively query the video (via learned actions) rather than passively reading a fixed set of frames, it bridges the gap between static video QA models and interactive video agent systems.\n- Methodology - The two-phase training—supervised fine-tuning followed by RL—is well designed, with thoughtful reward structuring to prevent pitfalls like mode collapse. The Cognitive Consistency Verification module adds robustness, and ablation studies confirm that each component, including multi-turn reasoning and CCV, contributes to performance. The use of GRPO and carefully tuned rewards further support the method’s effectiveness.\n- Empirical Performance - The paper shows substantial improvements in both accuracy and efficiency across multiple long video reasoning benchmarks. FrameThinker outperforms all baselines while using significantly fewer frames—for example, achieving 76.1% on LongVideo-Reason with ~20 frames versus 72.0% with 512. This efficiency makes it well-suited for scaling to longer videos.\n- Impact - This work addresses a key bottleneck in video AI – the inability to efficiently handle long videos by enabling models to focus on relevant frames, offering a scalable solution for long-horizon video analysis. Its integration of reinforcement learning into vision-language models introduces a flexible inference strategy with potential impact beyond video.", "weaknesses": "- Related Work - This section should have an explicit section on video key frame selection/sampling, given the focus of the paper. Quite a few key papers are missing from the discussion [a,b,c,d] \n- Frame Selection Ablation - One potential concern is the lack of comparison to other frame selection strategies. The paper convincingly shows improvements over uniform sampling and static baselines, but we don’t see comparisons to any heuristic or learned frame selection method [a,b,c,d]\n- Efficiency - The paper does not report actual inference time or compute cost comparisons. It’s assumed fewer frames = faster, but the iterative process might introduce some overhead (multiple forward passes). Quantifying the real-time speedup (or trade-off) would strengthen the empirical claims of efficiency.\n- Evaluation Scope - The experiments, while extensive on the benchmarks provided, focus mainly on QA tasks. It’s not fully explored how the approach would perform on other types of long video understanding tasks\n- Failure Cases - The paper would benefit from a clearer discussion of failure cases, such as missed events due to poor initial scans or premature stopping. It’s unclear how often issues like over-exploration occur or whether the Cognitive Consistency Verification (CCV) module mistakenly blocks valid strategies. Providing insight into these patterns and how frequently CCV intervenes would help assess the method’s robustness and reliability.\n\nReferences. \n- [a] M-LLM Based Video Frame Selection for Efficient Video Understanding, CVPR 2025\n- [b] VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection, CVPR 2025\n- [c] Vila: Efficient videolanguage alignment for video question answering, ECCV 2024\n- [d]  Self-Chained Image-Language Model for Video Localization and Question Answering, NeurIPS 2023", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Neqk9WNbjr", "reviewer": "Reviewer_U4yG", "summary": "This paper explores the task of long-video reasoning in Large Vision-Language Models (LVLMs). The author’s key motivation is that most existing LVLMs process frames uniformly sampled at a fixed interval, which often leads to many irrelevant frames being processed. To address this, they propose FrameThinker to enable LVLMs to perform frame sampling through a learned reasoning process. FrameThinker is trained in two stages: Supervised Fine Tuning (SFT) to teach structured thought-action generation, and Reinforcement Learning (RL) with GRPO to refine capabilities learned in the SFT stage. They conduct an extensive study on the RL stage, showing that unconditional and format-based rewards cause training collapse, and propose the Cognitive Consistency Verification (CCV) module to enforce alignment between thoughts and actions to stabilize the RL training.", "questions": "Can the authors clarify the evaluation protocol of the Qwen2.5-VL baseline (see Weakness 1)? If it is zero-shot, is there a reason why Qwen2.5-VL cannot be fine-tuned under the same conditions as FrameThinker?\n\nHow does FrameThinker handle cases where uniformly sampled frames dont capture any query-relevant frames (see Weakness 2)? I imagine it would randomly select a frame interval on which to “zoom in”, does FrameThinker have the ability to “zoom out”?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "The problem is well motivated as uniform sampling quickly becomes impractical as video tasks increase in complexity. As complexity increases, LVLMs will require the ability to reason about which frames to process rather than relying on dense or uniform sampling\n\nFrameThinker is sufficiently different from existing frame sampling methods the reviewer is aware of, which rely on heuristics/pre-trained models or decouple training of the frame sampler and LVLM. In contrast, FrameThinker directly optimizes the LVLM to perform frame sampling\n\nThe method is presented clearly though the writing, and its effectiveness is shown on well-chosen benchmarks", "weaknesses": "Concern with comparisons to baseline model\n* It seems like the baseline model (Qwen2.5-VL-7B) is evaluated zero-shot on the Video-Holmes and LongVideo-Reason datasets, but FrameThinker is first fine-tuned on these two datasets. The reviewer believes a true fair baseline in Table 1 would be Qwen2.5-VL fine-tuned on the Video-Holmes dataset, and In Table 2 and Table 3 it should be the Qwen2.5-VL fine-tuned on the SFT+RL instruction pairs without the thinking and action reasoning\n\nFrameThinker still relies on uniform sampling in its initial spare scan and uses them to decide where to “zoom in” and focus. Given that a max of 12 frames are sampled, it is not unlikely that all of these frames will be irrelevant in some cases, especially for queries like the one in Figure 11 that are not contextualized with temporal information. In these cases, FrameThinker will suffer from the same limitation of uniform sampling as existing LVLMs\n\nMinor comments on formatting:\n* Line 26: “FrameThinker get” should be “FrameThinker gets”\n* Notation: In Section 3.1 the query is defined as $i$ but in Section 3.2 the query is defined as $q$ (the $i$ also becomes a subscript instead of a superscript of Tau in Section 3.2). It seems to me like $i$ should correspond to a specific trajectory rollout and not the input query\n* Suggestion for Table 2 and Table 3: It might be better if the deltas are green instead of red as Red makes the differences appear negative. The best performing models can also be bolded (consistent with Table 1)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "qQ6vTnCfp5", "reviewer": "Reviewer_Qf4G", "summary": "Uniform frame sampling and static textual reasoning are the key problems of inefficiency and ineffectiveness in long video reasoning. This paper, FrameThinker, proposes a two stage training approach which consists of a supervised fine-tuning stage to enable the model to make actions (i.e, selecting frames), and a reinforcement learning stage to learn a policy for action decision making. Experiments show that FrameThinker greatly improve the performance over the baseline with much less frames processed, demonstrating the strong effectiveness and efficiency.", "questions": "1. Benchmarks in the paper are all long-video understanding and reasoning. Will training the model (i.e., FrameThinker) negatively impact the short-video reasoning, and traditional tasks for vision language models?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Strong performance over baselines presented in the paper, not only in accuracy, but also efficiency. Evaluations on diverse benchmarks show strong generalizations in the video reasoning.\n2. The two stage training, supervised fine-tuning and reinforcement learning, is typical applied in training test-time reasoning in large language models for math and coding problems. This paper expanded the domain to video understanding and reasoning.\n3. For reward design, a novel Cognitive Consistency Verification (CCV) module to verify that the actions from the model are logically grounded, interpretable and aligned with its reasoning. Ablation studies show that CCV is crucial to the performance.\n3. The figures are well-designed and the paper is easy to follow.", "weaknesses": "Missing video agent baseline: there were already some papers adopted a similar high-level idea of selecting video segments/frames for long video reasoning in a coarse-to-fine manner [1]. Therefore, adding at least one video agent baseline can make the contribution stronger if showing superior performance over existing methods.\n\nReferences:\n[1] Yang, et al. Video Curious Agent for Long Video Understanding. 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)