Video-KTR: Reinforcing Video Reasoning via Key Token Attribution
Authors: Ziyue Wang
Sheng Jin
ZHONGRONG ZUO
Jiawei Wu
Han Qiu
Qi She
Hao Zhang
Xudong Jiang
Venue: ICLR 2026 Poster (2026)
Keywords: Video Reasoning
Modality-aware Attribution
Reinforcement Learning
Multimodal Large Language Models
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Video-KTR applies modality-aware token-level RL for video reasoning, reinforcing only visual, temporal, and uncertain tokens. It boosts accuracy and interpretability, reaching 42.7% on Video-Holmes (above GPT-4o) with broad benchmark gains.

Abstract: Reinforcement learning (RL) has shown strong potential for enhancing reasoning in multimodal large language models (MLLMs), yet existing video reasoning methods often rely on coarse sequence-level rewards or single-factor token selection. Such approaches neglect fine-grained links among visual inputs, temporal dynamics, and linguistic outputs, limiting both accuracy and interpretability. We propose Video-KTR, a modality-aware policy shaping framework that performs selective, token-level RL by combining three attribution signals: (1) visual-aware tokens identified via counterfactual masking to reveal perceptual dependence; (2) temporal-aware tokens detected through frame shuffling to expose causal and temporal sensitivity; and (3) high-entropy tokens signaling predictive uncertainty. By reinforcing only the union of key tokens, Video-KTR focuses learning on semantically informative, modality-sensitive content while filtering out low-value tokens. Across five challenging benchmarks, Video-KTR achieves state-of-the-art or highly competitive results—42.7% on Video-Holmes, surpassing GPT-4o—with consistent gains on both reasoning-centric and general video understanding tasks. Ablation studies verify the complementary roles of the attribution signals and the robustness of targeted token-level updates. Overall, Video-KTR improves accuracy and interpretability, offering a simple, drop-in extension to RL for complex video reasoning.

Published as conference paper ICLR 2026
VIDEO-KTR: REINFORCING VIDEO REASONING VIA KEY TOKEN ATTRIBUTION
Ziyue Wang Sheng Jin', Zhongrong Zuo', Jiawei Wu',
Han Qiu', Qi She!, Hao Zhang*, Xudong Jiang
BytteDance
"Scooo Electrical and Electronic Engineering, Nanyang Technological University
NNational University of Singapore
*College of Computing and Data Science, Nanyang Technological University
ziy ciyue .nt.tuue. edu. sg, hzhang26@outloook com, exdjiangentu. edu. ag
ABSTRACT
Reinforcement learning (RL) has shown strong potential for enhancing reasoning in multimodal large language models, yet existing video reasoning methods often rely on coarse sequence-level rewards single-factor token selection, neglecting fine-grained links among visual inputs, temporal dynamics, and linguistic outputs, limiting both ac- euracy and interpretability. We propose Video-KTR, modality-aware policy shaping framework that performs selective, token-level RL by combining three attribution sig- nals: (I) visual-aware tokens identified via counterfactual masking reveal percep- tual dependence; (2) lemporal-aware tokens detected through frame shuffling to ex- pose temporal sensitivity; and (3) high-entropy tokens signaling predictive uncertainty. By reinforeing only these key tokens, Video-KTR focuses learning on semantically in- formative, modality-sensitive content while filtering out low-value tokens. Across five challenging benchmarks, Video-KTR achieves state-of-the-art highly competitive re- sults 42.7% on Video-Holmes (surpassing GPT-40) with consistent gains on both rea- soning and general video understanding tasks. Ablation studies verify the complemen- tary roles of the attribution signals and the robustness of targeted token-level updates. Overall, Mideo-KTR improves accuracy and interpretability, offering simple, drop-in in extension to RL for complex video reasoning. Our code and models are available at hctps: /tte:///gitaan com/ http///wwwnngg///ioeooooooo
INTRODUCTION
Reinforcement learning (RL) has emerged as a powerful paradigm for enhancing long-chain chain reasoning in large language models (LLMs) (OpenAl 2024; DeepSeek-Al Al 2025 Yang et al. 2025). In particu- lar, GRPO (Shao et al. 2024) exhibits strong performance on complex reasoning tasks. Building on this success, RI. has been extended multimodal LLMSs (MLLMs), achieving notable gains visual under- standing through sample diversification (Wang et al. 2025a Leng et al. 2025). tailored reward design (Tan ctt al. [2255] Shen et al. 2025), and advanced optimization strategies (Deng al. 2025 Zhang al. [025 Dang et al. 2025). Yet, these advances primarily target static images, leaving video reasoning compara- tively underexplored. Recent work mitigates this gap by integrating temporal supervision, e,g., contrasting
'Correspondence Hao Zhang and Xudong Jiang.

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Published as a conference paper at ICLR 2026
predictions on ordered and shuffled frames, to strengthen temporal sensitivity (Feng et al. 2025). and by embedding
priors into reward mechanisms to better align spatial grounding with temporal ordering (Li et al. 2025b) [Snn et al. (025). Other studies build RL-driven tool agents capable of decompos- ing and solving long-form form video queries (Tian et al., (025), as well as scalable reward designs that adapt to video complexity via difficulty-wware or temporally robust objectives (Park et al. 2025
Chen et al. 2025b).
However, video reasoning still struggles to accurately model temporal dynamics and exploit visual cues. Many approaches overlook explicit temporal dependencies and causal structures-essentie for effective rea- soning (Tian et al., 2025] Huang al.) 2025). Some methods (Feng al. (025) introduce temporal-specinne constraints, such as penalizing predictions on shuffled frames, yet these rely on coarse global assumptions. This design ignores tasks solvable by static cues, introducing optimization noise that may hinder training. Moreover, many approaches fail to ensure fine-grained semantic alignment between visual inputs and output tokens. Without explicit modeling of modelity-specific dependencies, models underutilize visual evidence and over-rely on linguistic priors (Wang et al., (025d] Li et al., 2025b), increasing hallucination risks. Even token-level RL prioritizing high-uncertainty tokens via entropy (Wang et al. 20256) often lack modality awareness, limiting their ability to capture visual-tempora dependencies. The absence of precise token- level correspondence across modalities not only reduces reasoning accuracy but also limits interpretability.
To address these challenges, we propose Video-KTR, a modality- policy shaping framework for leken-level optimization in video reasoning. Unlike prior methods that impose coarse, global temporal con- straints (Feng et al., 2025 Dang et al. 2025) or rely solely on entropy-based token selection (Wang et al.) 2025b), Video-KTR explicitly models token sensitivity to visual and temporal perturbations, updating only critical tokens. We categorize these tokens as: (1) visual-aware tokens, identified via counterfactual mask- ing to capture reliance on perceptual input; (2) temporal-awar aware tokens, detected through frame shuffling to reflect sensitivity to temporal order and causality; and (3) high-entropy tokens, characterized by predictive uncertainty, complementing the first two categories. This selective updating focuses learning capacity on the most informative reasoning steps, reducing interference from redundant tokens. As result, Video-KTR significantly enhances both accuracy and integrated multimodal reasoning in complex video tasks.
To assess Video-KTRR we conduct extensive experiments on representative video reasoning benchmarks. Re- sults demonstrate SOTA or highly competitive performance across datasets. On Video-Holmes, our method achieves overall accuracy of 42.7%, surpassing GPT-4o (42.0%). Moreover, ablation experiments show that modality- aware token attribution, selective reinforcement learning, and targeted perturbation jointly en- hance the exploitation of visual and temporal cues. These findings confirm that Video-KTR's gra-ggained, token-level optimization provides an effective and interpretable approach to advancing video reasoning.
Our contributions are twofold: We employ counterfactual analysis to unveil modality sensitive critical tokens in video reasoning, and propose Video-KTR tailored for token-level optimization in video reasoning tasks. To the best of our knowledge, this is the first work to integrate modality-awan token selection into RL. for video reasoning. 2) Video-KTR achieves SOTA performance on multiple video reasoning bench- marks, including 42.7% on Video-Holmes dataset-matching GPT-4o and significantly outperforming ex- isting open-source baselines. Ablation studies confirm the effectiveness of our modality-attiibtioon signals, token selection strategy, and framework design, alongside the robustness and generalizability of Video-KTH
2 RELATED WORK
Reinforcement learning has emerged powerful paradigm for enhancing the reasoning abilities of
LLMS (OpenAl 2024 DeepSeck-All [025 Yang et al.] 2025). while GRPO (Shao et al.) 2024) and its variants particularly inffuential in optimizing outcome-level correctness. Upon these advances, RL has been increasingly extended to multimodal LLMS (Wang et al. 2025c Huang el. 2025 Leng et al. 2002)]
Meng et al.) 2025
Yuan et al. 2025 Zhang et al. 2025 Chen et al. 2025a). However, these methods re-
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Policy Model
Rollout
Original Rollout Tokens
"Whch principle Introdaced last in the v'deo?"
Update
Joint modeling of visual, temporal, and uncertainty signals
Vision-Awart Counterfactual Reasoning
Tomporal-Aware Counterfactual Ressoning
2 Unsertaindy- Entropy Analgis
Shaffted
Legit
Legits
Voaa Takeea Declate
Talmm: Cockate
High- Hije-Fetropy Tallnn, Ricluds Tainns
Figure 1: Overview of the Video-KTR framework. The model identifies key tokens based on entropy, visual, and temporal signals, and updates only those tokens during reinforcement learning.
main centered visual perception, with limited attention to temporal dynamics. Recent research proposes specialized RL strategies tailored for video reasoning, including temporall--aware fine-tuning by contrasting predictions from shuffled versus ordered frames (Feng al. 2025), enhancing spatio-temporal reasoning via structured rewards (Li et al. 2025b: Sun et al. 2025), introducing tool-based agent frameworks for handling long-form video tasks (Tian et al., 2025), and developing scalable reward designs such as difficulty awware regression and robust temporal reward formulations (Park et al. 2025 Chen et al. 2025b). Despite notable progress, these methods predominantly rely on coarse sequence- rewards or limited token-level selec- tion methods without explicit modeling of modality-speciff dependencies. In contrast, we propose to jointly model complementary visual, temporal, and high-entropy token attribution signals, enabling more precise lekenllevel credit assignment and improving reasoning performance. To the best of our knowledge, this is the first work to unify multiple token attribution strategies for modality-sensitive RL in Video-MLLLMs.
3 METHODOLOGY
Recent RL work for Video-MLLMs (Feng et al. 2025] Chen et al., 2025b) mainly relies on coarse sequence- level rewards simple token-level heuristics that capture only predictive uncertainty, without modeling video-specific structures like visual-temporal coherence or causal dependencies. In this work, we propose Video-KTR, modality-aware policy shaping framework (Figure [ that performs fine-grained RL through token-level attribution. We use counterfactual analysis to identify tokens conditioned on visual cues and temporal order, and exploit predictive entropy detect reasoning-critical tokens. By selectively updating these tokens, Video- KTT amplifies ceasoning-sensittee signals, improving both accuracy and interpretability.
3.1 MULTT--ERSPECTII TOKEN IMPORTANCE ANALYSIS
A central challenge in video reasoning is identifying which outputs truly rely on visual or temporal cues. Prior attribution methods, such entropy-based approaches (Wang et al. 2025b), expose uncertain tokens but cannot distinguish modality-specifit dependence from generic reasoning difficulty. To address this, we propose a modality- aware attribution framework that jointly captures visual dependence, temporal sensitiv- ity, and predictive uncertainty, enabling precise identification of key reasoning tokens.
Visual-Aware Tokens. Tokens closely associated with visual input are vital for multimodal reasoning, as they align language outputs with perceptual evidence and enable key functions like reference resolution,
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object recognition, and appearance description. These capabilitiess are particularly important in tasks that in- volve spatial localization, sppearance-based inference, and dynamic scene understanding. Effective targeted learning thus hinges on accurately identifying output components that genuinely rely on visual information.
To identify these disual-award tokens, we introduce visual attribution mechanism based on counterfactual perturbation, which quantifies each token's sensitivity to visual input by masking the video and measuring the resulting shift in logits. Given video input v and textual prompt t, the model generates response sequence ğ = (V1: 1/2. ................................ Vr), with decoder logits 2; for each token y/- We first compute logits under both full-context and masked-visud settings, extract the target token's 's logit each position as:
visible fo ycs. v,t), 2 masked = fo Vet. v,t).
where So is the decoder network, denotes the original visual input and v = 0 indicates visual features are masked. Then, the visual attribution score is calculated by the log-pro oog-robbability shift as:
vis log softmax (x."ttmaxxx..)))....))))))
log softmax (2 (
where A assesses the visual dependence of i-th token. A larger A indicates that the prediction of token y: is strongly dependent on visual input. Depicted in Figure [a tokens such as "person," " "dooo,,"""oor",,, " and "blue" exhibit high visual sensitivity and are prioritized during training strengthen perceptual grounding.
Temporal-Aware Tokens. Tokens sensitive to the temporal structure of videos are crucial for understand- ing event order, causality, and temporal dynamics. They allow the model to distinguish temporal relations, reason about progression, and track changes over time. To enhance temporal reasoning. it is essential to identify output tokens significantly affected by the sequence of visual events. Unlike T-GRPO (Feng al., 2025). which applies sequence-level penalties to temporally shuffled inputs, we instead compute logit shifts induced by frame shuffling locate tokens most responsive to temporal cues, enabling more fine- tineggrained and targeted model optimization. To identify temporal-aware tokens, we disrupt the input's temporal or- der by shuffling the video frames, thereby breaking the original event sequence, and compute the resulting token-wise logit shifts. Given video sequence
(U1, (2,,,,,.... UF) and a textual prompt t, the model generates a response sequence O = (31, (3/1,/22... .yr), with decoder logits z; for each token yi- Similarly, we extract the target token's 's logit each position as:
2 ordered = fo(v Dei. v,t,, zi
shuffled = fo y<i; v.t),
when fo denotes the decoder network, and the temporal attribution score for each token is computed as: A temp log softmax(zę0lerd) gnderee)) log (oztmax(2)haalel))
Similar to visual-aware tokens, a larger A.mp indicates stronger reliance on the video's temporal structure. Tokens such as "first," "then," and "appear" with high A (Figure 2bp are highly sensitive to event progres- sion and transitions, making them essential for modeling event flow and long-range dependencies beyond isolated frames. This attribution mechanism not only pinpoints temporally sensitive reasoning cues but also highlights where temporal understanding most critical. In video question answering. many queries depend on capturing action sequences and event relations. For instance, answering "What did the person do after entering the room?" requires precise temporal reasoning. Our temporal-aware attribution explicitly reveals such dependencies, guiding reinforcement learning toward causally significant moments.
While visual and temporal attributions capture modality-speciffi dependencies, they may miss caasooninn-crtical tokens unrelated to perception sequence. Thus, we incorporate predictive entropy (Wang et al. 2025b) as a logic-aware criterion to identify low-confidence tokens:
Entropy-Aware Tokens.
H(1 = Σ p( = w) log p(z, = =w)
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COM
something
necdoh g vervel
Lven
seem Hmm
show
sugges
OO
ue
option ooobject
people
moving Mn
Wait NOW
marecaoreful
see
ed magg SpRiEe
Maretu needi
anteet loo Ieeclook
image
S Sm
(a) Visual-aware tokens.
(b) Temporal-awer aware tokens.
(c) Fntropy-aware tokens.
Figure 2: An illustration of critical tokens identified by each attribution strategy.
where w denotes token index. High-entropp tokens-e4 e.g.. "however," "wait" mark discourse pivots or decision points critical for reasoning. Illustrated in Figure 2c they capture semantic ambiguity, conflicting cues, or implicit causality, and offer logic-aware means of identifying reasoning signals beyond visual and temporal cues. We incorporate into token-level credit assignment as an empirically validated strategy.
3.2 TOKEN SELECTION AND POLICY UPDATE
Building on the token importance estimation, we explore how to integrate this information into the RL pro- cess effectively. The central challenge lies in how to leverage the identified key tokens to enhance the train- ing efficiency. Conventional RI frameworks like GRPO treat all tokens equally during reward assignment, which dilutes learning particllyyy in long video reasoning sequences- and thus hampers training efficiency. We introduce token-based policy shaping mechanism that selectively reinforces semantically critical tokens during training. To be specific, we select the top r% of tokens from each attribution strategy and take their union, S Svis U Stemp U Sent. as the set of key reasoning tokens. These tokens collec- tively capture the most essential semantic cues for visual grounding, temporal understanding, and decision uncertainty, thereby enabling more focused and efficient policy updates.
We adopt the GRPO framework (Shao et al., 2024), which improves the current policy my by contrasting its outputs with a set of reference completions sampled from the previous policy Teag Given a batch of G questions q ~ P()) and corresponding rollouts 01 ~ T00aa--4)) the GRPO objective is defined as:
G.
loil
clip(rre, e,, 11++)) (11+++AAii) A..)
JARPO (or)
T
min (rie
(I)
Oj
where the KL divergence term is omitted for simplicity, ri. Teas (o..
1ol00,19.00.tt) is the token-level likelihood
ratio, and A,, denotes the advantage estimate computed via the normalized group reward for all tokens in o To prioritize semantically important reasoning tokens during learning, we apply binary mask m,, E {0, 1) selectively update tokens. This mask is constructed from the union of top-ranked tokens identified by visual- temporal-aware, and entropy- aware attributions. The resulting objective is:
G
ladl
may min (TLe Ar,,, clip(rr,, 1 e,1 A..))
JVidco-KTR (0) -0)= [oz
(2)
G
where only tokens with wa, 1, i.e. those identified as important, contribute to the loss, allowing the model focus supervision on semantically rich segments such as temporally anchored verbs, visually descriptive nouns, and discourse pivots with high uncertainty. As shown in Figure 2 the selected tokens differ across attribution strategics, reflecting their distinct roles in multimodal reasoning. In contrast, unselected tokens are largely low-information function words (e.g., auxiliary verbs, pronouns, determiners, prepositions, and punctuation), as illustrated in Figure 6b] Their prevalence further validates the attribution mechanism's ss ability filter redundant content and enhance learning efficiency.
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Table Performance comparison across crasoningg-rientte and general caneras-purpose purpose video benchmarks, where indicates scores evaluated by ourselves.
Video Reasoning Benchmark
Video General Benchmark
Models
Size
Frames
Video-Holmes
VideoMMMU MMVU(me)
TempCompass
VideoMME
Proprictary MLLMs
GPT-4o
42.0
61.2
75.4
73.8
71.9
GPT-S
46.7"
84.6
82.6"
83.3"
86.7
Gemini-1.5-Pro
41.3
53.4
71.2
67.1
75.0
Gemini-2.5-Pro
45.0
83.6
78.4"
84.3*
84.3
Open-Source MILMs
LLaVA-OV
7B
64
33.8
49.2
64.2
58.2
VILA-1.5
8B
64
33.8
49.2
58.8
58.2
Qwen2.5-VL
7B
27.8
47.4
59.2
67.9
65.1
Video-R1
7B
32
36.5
52.3
63.8
73.2
59.3
Video-RTS
7B
51.2
40.7
52.7
66.4
63.0
TW-GRPO
7B
32,9
51.3
65.8
73.3
55.1
SFT Models
16
31.7
47.4
61.3
69.2
52.8
Qwen2.5-VL-SFT 7B
32
33,9
49.4
63.5
69.9
55.4
64
33.7
49.4
61.6
70.0
58.8
16
40.7
51.3
65.7
73.3
57.3
Video-KTR
7B
32
41.6
52.6
65.9
73.4
60.3
64
42.7
53.1
66.6
73.5
62.5
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. For RL data, select the raw data released by Fi (Feng et al. 2025), containing 260K
samples with verifiable answers. We conduct strict difficulty sampling to enhance the overall difficulty and coverage of the training samples. Specifically, accuracy over 8 tails using Video-RI-SFT (Feng et al. 2025), leading to ~ 15K high-quality subset. We also incorporate 1.5K training examples of Video-Holmes (Cheng et 2025 to form the final training
remove samples that achieve above 80% below 20%
The Video-KII KTR directly perform RL training on the Video-RI SFT checkpoint, which itself built upon the Owen-2.5-VIL-7B (Bai et al. 2025) backbone. To improve the efficiency, we limit the input to maximum of 16 frames with maximum resolution of 128 x 28 x 28 pixels. We set the learning rate to 2e - 6, the global batch size to 32, the rollout batch size to 256, the maximum sequence length to 16384; the KL divergence coefficient (3) is configured at 0.4; and 8 responses are sampled for each prompt with temperature of 1.0. During the inference, we may extend the maximum frames to 64 with a maximum resolution of 256 x 28 x 28.
Implementations.
Benchmarks.
To comprehensively assess the performance Video-KTRR we select five challenging video benchmarks: (1) massoning-orientee benchmarks, including Video-Holmes (Cheng al. 2025). VideoM-
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MMU (Hu et 2025), and MMVU (hao al. 2025). assess the temporal, causal, and knowledge-
intensive reasoning capability of MLLMs; (2) general understanding benchmarks, including TempCom- pass (Liu al. 2024) and VideoMME (Fu al. 2025). Note Video-Holmes is a newly established benchmark that poses significant challenges, requiring models to integrate dispersed narrative cues from suspenseful short films and perform complex deductive reasoning beyond basic visual recognition.
Baselines. We compare Video-KTH against a diverse set of baselines, encompassing both general-puree purpose and reasoning-oriented MLLMs of similar size: LLaVA-OneVision (LLaVA-OV: [i et al. (2025a)), VILA- 1.5 (Liu et al. (2025), Qwen2.5-VL. VL (Bai et al. 2025), Video-R1 (Feng et al. 2025 Video- RTS (Wang et al.
2025d), and TW-GRPO (Dang al. 2025).
4.2 RESULTS AND ANALYSIS
Comparison with state-of-the-art models. We evaluate Vidco-KTR against a diverse set of video rea- soning models on three reasoning and two general video understanding benchmarks. Reported in Table T Video-KTT Meeo-KTRRaa consistently achieves of-ieof-the-rrt results. On Video-Holmes which targets high-level tem- poral and social reasoning short films-on our model attains 42.7% surpassing all open-source baselines and closely matching proprietary models like GPT-5 (OpenAl (2025); 46.7%) and Gemini-2.5-Pro (Comanici jet al. (2025); 45.0%). On knowledge-intensive benchmarks, ks, i.e., MMVU(me) and VideOMMMU, Video- KTR achieves 66.6% and 53.1%, respectively, highlighting its strength in complex reasoning. Video-KIT also delivers competitive performance on general video understanding, showing that improved reasoning does not compromise broad comprehension. Notably, accuracy increases steadily with more input frames (16 to 64), underscoring the scalability and robustness of our token-aware RL for longer temporal sequences.
T-CRPO
Ti diin Wine d71
Mitec-KTR
TWK-SIPO
52
12.
1%
28.
TG
INC
(a) Ccoss-Benchmate Comparison.
(b) Performance across Video-Holmes subtasks.
Figure 3: Performance comparison on (a) video reasoning benchmarks and (b) detailed subtasks of the Video-Holmes benchmark. Subtask abbreviations: SR Social Reasoning, IMC = Intention & Motive Chaining. TCI = Temporal Causal Inference, TA = Timeline Analysis, MHR Multimodal Hint Reasoning, PAR Physical Anomaly Reasoning, CTI Core Theme Inference.
Research Question (RQ1): How effective is Video-KTR compared with other video RL methods? To isolate the effect of training data and recipe, we conduct controlled study where all models are trained under an identical dataset and training recipe. Depicted in Figure Ba| Video-KTR consistently outperforms vanilla GRPO, T-GRPO. and TW-GRPO, demonstrating its effectiveness independent of data quality quantity. Figure reports Video-Holmes results by subtask. Video Fideo-KTR& achieves marked gains in Timeline Analysis (TA) and Core Theme Inference (CTT), tasks that require advanced temporal reasoning and holistic content understanding. The results confirm that our method enhances both temporal and structural reasoning, vali- dating the token-aware training design. A qualitative comparison with T-GRPO (Appendix B.6) illustrates
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Table 2: Performance of token selection using entropy (E), visual (V), and temporal (T) attributions.
Strategy
E
Video-Holmes
Video-MMMU
MMVU
Avg
Vanilla GRPO
X
X
38.8
49.8
64.8
$1.1
E
V
X
39.5
49.2
64.3
51.0
V
X
V
40.5
51.9
65.1
52.5
T
X
42.1
50.1
65.5
$2.6
V
X
V
V
41.3
52.1
65.2
52.9
E&T
V
X
V
39.6
50.9
64.3
51.6
&&E V&E&T
V
41.0
51.1
66.4
52.8
41.6
52.6
65.9
53.4
that Video-KTR identifies key visual and temporal cues more effectively, leading to correct predictions and highlighting the benefit token-level optimization in aligning model attention with critical signals.
RQ2: What is the impact of different attribution signals video reasoning performance? To assess the impact of different attribution signals, we ablate the three token types entropy-aware (E), visual- aware (V), and temporal-awee (T) as shown in Table Each signal individually outperforms vanilla GRPO, with temporal-awer tokens giving the largest gain on Video-Holmes, reflecting their importance for se- quence reasoning. However, using temporal-aware tokens alone hurts performance other benchmarks (e.g., Video-MMMU), suggesting that a single signal can bias the policy toward a narrow reasoning mode. Pairwise combinations yield further improvements, and the full E+V+T setup consistently achieves the best results across benchmarks. These findings show that the three signals are complementary and that jointly leveraging them enables more accurate and robust policy optimization.
RQ3: What are the linguistic divergences in token selection across attribution strategies? To assess the complementarity of our attribution strategies, analyze the linguistic distribution and overlap of se- lected tokens. As shown in Figure 4 visual-aware tokens are mostly NOUNs (24.8%), consistent with their role object grounding. Temporal-aware tokens emphasize VERBS (21.2%) and PRONs (11 (11.0%), reflect- ing action transitions and temporal references. Entropy-aware tokens contain more ADVs (8.8%), capturing discourse modulation and uncertainty. These patterns suggest that modality-awan token selection leverages complementary reasoning dimensions perceptual grounding, temporal structure, and uncertainty cues to enhance video understanding.
Miual
Visual-Aware Tokens
Entropy-Aware Tokens
popprto 150
23.4%
13.2%
42
10.5%
AMC
3.3%
4.1%
20.0%
NDON
VE.88
ADI
ADV
FRON
Temporal
Figure 4: POS ratio across selection strategies.
Figure 5: Distribution of updated tokens.
As illustrated in Figure 6a the word cloud of selected tokens highlights semantically rich that directly contribute to multimodal reasoning, such as content words related to objects (object, person), actions (ap- pear, hold), and reasoning markers (wair, let, consider, finally). These tokens capture critical visual and
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10
Ject
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LOOK
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Hmmm
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Seec
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(a) Word cloud of selected critical tokens.
(b) Word cloud unselected low-value tokens.
Figure 6: Comparison of tokens selected reasoning crittical and unselected tokens. Selected tokens centrate on semantically informative content, whereas unselected tokens are dominated by function words, indicating that the selection mechanism emphasizes reasoning-critical tokens while filtering low-value ones.
LogProbDirt
1. Norm
531
$2.5
Divergence
51.3
$1.9
52.0
Lhear
Divergence
Exponential
51.3
Hellingr
$2.4
50
61
12
51.0
$1.5
12.0
12.5
$3.0
53.
50.0
Video Reasaning Benchmark Average Score
Video Reasoning Benchmark Average Score
Figure 7: Benchmark Scores under Different Token Weighting Methods.
Figure 8: Benchmark Scores under Different Dis- tance Calculation Methods.
temporal evidence, guiding the model to focus on informative regions of the input. In contrast, as shown in Figure (6b] unselected tokens are dominated by oowiifoommttin function words, including auxiliary verbs (AUX: is, have), pronouns (PRON: I, we), determiners (DET: the, this), prepositions (ADP: in, or), and various punctuation or formatting symbols (e.g.. ...). Their prevalence confirms the effectiveness of our attribution strategies in filtering out syntactic noise while prioritizing semantically salient reasoning cues.
RQ4: How do token weighting strategies affect selective RL? We compare several token- weighting strategies to examine whether soft importance modulation can improve upon the hard selection used in Video-KTR. The methods include: (1) Binary Top-20%, which updates only the top 20% key tokens and masks the rest: (2) Softmax Weighting, which normalizes importance scores into probability distribution; (3) Sigmoid Weighting, which applies smooth nonlinear mapping: (4) Linear Weighting, which scales updates proportionally to token scores; and (5) Exponential Weighting, which accentuates score gaps. As shown Figure 7 the hard top-20% strategy consistently outperforms soft weighting variants, indicating that strict selection yields clearer credit assignment while soft modulation spreads updates over less relevant tokens. Token-level overlap analysis (Figure 55) further shows that, after applying the union of visual, tem- poral, and entropy signals, the final update ratio reaches about 40%, with most tokens unique individual strategies. This demonstrates that each attribution signal captures a distinct and complementary subset of eeeloiannreeeaantttt tokens.
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Figure 9: Effect of update ratios.
Figure 10: Effect of perturbation strategies.
RQ5: Do distributional shifts improve token attribution beyond log- pag-prbbabilit? To identify informa- tive tokens for reinforcement updates, we assess both changes in predicted log-probabilities and shifts in the full output-logit distribution under modality-specífic perturbations. Specifically, we quantify token-wise dis- tances between pre- and post-perturbation logits using metrics like L1/L2 norms, KL/JS divergences, cosine similarity, and Hellinger distance. Depicted Figure g] the simple bog-probability difference consistently outperforms these alternatives. It is robust, easy implement, and efficient-requirin only subtraction of two log-probabilities per token-making it highly scalable. These results suggest log- log-probability differences reliable and efficient signal for tracking prediction-confidence shifts and identifying informative tokens.
RQ6: How does the token-level update ratio affect performance? We ablate the token-level update ratio- the proportion of key tokens receiving full gradient updates-wat all token types applied jointly. As shown in Figure / accuracy follows a double-peak pattern and achieves the best performance at a 20% ratio. Performance drops higher ratios (30-50%), indicating that moderate selective updating is optimal, while overly large update sets introduce noise and reduce stability.
RQ7: Do perturbation strategies influence token identification and model performance? To assess the robustness of our token identification method, examine alternative perturbation strategies for visual and temporal dependencies. For visual perturbation, we consider masking all frames (default), masking half, and replacing frames with unrelated contens. For temporal analysis, we compare random shuffling (default), sequence reversal, and segmental shuffling. In all cases, the token update ratio is fixed at 20%. Depicted in Figure all variants achieve comparable performance, with the default Betting--full-rranee masking (52.5) and random shuffling (52.6)- performiin slightly better. These results suggest that perturbation strength has limited impact, confirming the robustness of our method. We therefore adopt the default strategies for their simplicity and reliability, without requiring manual tuning.
5 CONCLUSION
We introduce Video-KTR, modality-aware policy shaping framework that enhances video reasoning by shifting RL from coarse sequence supervision to selective, token-level attribution signal assignment. By integrating visual, temporal, and uncertainty-basee based attribution, Video-KTR focuses on semantically critical tokens, improving both accuracy and interpretability. Experiments across five benchmarks Shinnarrk--hhhhihhttee highlighted demonstrate SOTA highly competitive performance.
by 42.7% Video-Holmes, surpassing Ablations confirm the complementary roles three signals and robustness of targeted updates. The approach integrates seamlessly into RL and enhances reasoning without compromising general video understanding.
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A LIMITATIONS
While Video-KTR has demonstrated notable improvements and potential, several avenues remain open for further exploration. First, under challenging conditions such as low light, occlusion, rapid motion, or OCR/ASR noise, visual-text alignment may be affected. Enhancing the stability and robustness of key token selection in diverse scenarios represents a promising direction for future work. Second, Our experi- ments have primarily focused on video reasoning benchmarks, whereas broader applications such as video captioning or temporal grounding have not yet been systematically examined. Extending validation to these domains would offer valuable insights into the generalization of the framework. Third, the current frame- work emphasizes vision-language modeling without explicitly incorporating additional modalities such as audio or motion sensor data. Since these signals often provide complementary information, integrating richer multimodal inputs holds considerable potential for future research.
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E c8ed
Gresters barn Elpaa Strngge
Thrrby
Traning
Thinipg
Figure 11: Video-KTR exhibits lower loss variance, stronger gradient signals, and higher gradient alignment during training. Due to the high variance of the accuracy curves, we smooth the curves using window smoothing with a window size of 20.
B MORE RESULTS AND ANALYSIS
B.1 GRADIENT DECOMPOSITION AND NOISE INTERPRETATION
To analyze whether masked tokens meaningfully affect optimization, we examine gradients at the final layer, where updates most directly influence policy learning. Let the sequence-level loss be I. Ee and denote the final-layer gradient for token by
gt V & = dl,
ah (L)
oh
[L) D0(L)
where h (2) is the last-layer hidden state and (F.) denotes all parameters of the final layer. We partition tokens into critical set S and residual set (unselected low-value tokens) R, yielding the following gradients
Yrall Σ
Σ yto
Frest Σ
gt:
9KTR
gh-
IES
LER
To assess how these components contribute to parameter updates, compare the directional agreement between each aggregated gradient and the full gradient. Rather than considering only their magnitudes, we evaluate whether two gradients point in similar directions in parameter space. This is measured using cosine similarity, written as cos cos(ga, (ga,gb) 96), which reflects how much one gradient aligns with the other: values close to 1 indicate that Ju and gb point nearly the same direction and thus provide mutually reinforcing updates. In practice, we compute CoS(T Iran) and cos(grest Itun) to quantify how much each component contributes the effective update direction.
As shown in Figure Video- -TR produces larger, cleaner, and more directionally consistent gradient updates compared vanilla GRPO. The mean gradient norm of the selected critical tokens is 4.50, which roughly three times higher than that of the masked tokens. Likewise, the directional agreement with the full gradient substantially stronger: the cosine similarity critical-token gradients averages 0.862, compared with 0.572 for the masked-token gradients. These results indicate that the selected tokens dominate the effective update magnitude and align far more closely with the model's underlying optimization trajectory. In contrast, gradients from residual masked tokens both much weaker and directionally inconsistent, suggesting that they contribute little meaningful learning signal and instead introduce noisy perturbations into the optimization process.
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Table 3: Performance comparison on image benchmarks. Results show that restricting updates to visual tokens already yields clear gains GRPO variants, highlighting the importance of visual token opti- mization. Nevertheless, the full Video-K KTR model achieves the best performance across all benchmarks, underscoring the additional benefits of integrating temporal and entropy signals.
MvMU(val)
A12D
MMSTAR
ChartQA
Qwen2.5-VL-SFT
42.89
75.68
53.07
69.92
Vanilla GRPO T-GRPO
44.33
76.30
53.60
73.08
44.67
77.51
54.32
77.04
KTR- Visual Tokens Only
46.44
79.27
57.38
77.08
Video-KTR
47.33
80.34
57.77
80.68
We further compare the training loss variance, which reflects overall optimization stability. Video-KTR achieves more stable training dynamics: its mean loss variance is 0,4436, lower than that of vanilla GRPO (0.5528) across training steps, supporting that masking low-information tokens reduces gradient noise and leads to more stable reinforcement learning updates.
Together, these results validate the effectiivnesss of our token-selection mechanism and highlight its role in guiding the model toward more meaningful and robust multimodal reasoning behavior.
B.2 ADDITIONAL EVALUATION ON IMAGE BENCHMARKS
To further assess the contribution of visual tokens, introduce variant of our method, denoted as KTR- Visual Mikens-OOly, where reinforcement learning updates are restricted to visual-related tokens while dis- carding temporal and high-entropy token signals. This setting isolates the effect of visual supervision and provides a controlled comparison against the full Video-KTR framework. As shown in Table 3 this vari- ant yields notable gains including MMMU-val (Yue et al., 2024). A12D (Kembhavi et al., 20166). MMStar (Chen et al. 2024), and ChartQA (Masry et al. 2022), underscoring the importance visual token optimization. Nevertheless, the complete Video-KII TTR achieves the best overall performance, suggesting that the integration of temporal and uncertainty- token signals further enhances eross-modal reasoning.
baseline GRPO methods several classic image understanding benchmarks,
B.3 COMPUTATIONAL COST ANALYSIS
Table 4: Comparison of training cost and forward efficiency. TT = training time, Mem peak memory, SPS samples per second, Lat. latency. Video-KTR introduces only marginal overhead compared with Vanilla GRPO and T-GRPO.
TT (h)
Mem (GB)
SPS
fwd Lat. (s)
fwd FLOPs per GPU(T)
Vanilla GRPO T-GRPO
4.7
77.9
0.946
4.70
116.6
5.1
78.3
0.872
4.89
128.6
Video-KTR
5.2
78.5
0.855
4.92
129,9
Our experiments were conducted on 32 H100 (80GB) GPUs, and the training Video-KTR was completed in 5.2 hours. As summarized in Table the additional computational overhead introduced by method remains modest. The key reason is that token importance scores are computed in a single extra forward pass, avoiding repeated per-token calculations. Compared with vanilla GRPO and T-GRPO, Video-K'TV
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0.6
Gaussian smooth (a= 2)
Per-percentile update prob.
0.5
Update Probability
0.4
0.3
0.2
0.1
0.0
20
40
80
100
Taken Position Percentile (1% - 100%)
Distribution of updated token positions in Video-KTR. Early tokens show higher update proba- bilities, while later tokens still receive non-negligible updates, indicating that the model continues refining its reasoning rather than relying solely on pretrained linguistic priors.
Figure 12:
shows almost increase in peak memory usage (77.9 GB - 78.5 GB) and only minor rise in forward latency (4.70 s - 4.92 s). The overall training time grows slightly from 4.7-5.1 hours to 5.2 hours, while forward FLOPs per GPU increase moderately (116.6 T 129.9 T). These results indicate that our token- level reinforcement strategy delivers significant performance improvements at marginal computational cost.
B.4 UPDATED TOKEN POSITION ANALYSIS
To better understand where reinforcement learning updates are applied, we analyze the distribution of up- dated token positions. As shown Figure 12 tokens at earlier positions exhibit consistently higher update probabilities than those at later positions. This pattern is expected: early tokens play larger role in de- termining the overall direction of the answer, whereas later outputs tend to be more deterministic and less influenced by input variability, Nevertheless, also observe neglliiiee updates in later positions, sug- gesting that the model does not merely rely on pretrained linguistic priors but continues to refine its reasoning based on the input content.
B.5 GENERALIZE VIDEO-KTR SMALLER MILM
To assess generalizability beyond the 7B-scale backbone, we apply our training strategy to the smaller Dwen2.5-VL-33B model. As shown Table our method consistently outperforms vanilla GRPO across all frame settings (16, 32, and 64) and benchmarks. These findings demonstrate that our token-aware rein- forcement learning strategy remains effective for smaller-scale MILMMs.
B.6 QUALITATIVE PERFORMANCE OF VIDEO-KTR
To qualitatively demonstrate Vidco-KTR's advantages, several representative examples from Video-Holmes are presented. In Figure [3 our model correctly identifies her ability to "ignore spatial physical limita- tions"-the the official answer by attending essential visual and temporal cues, such as her "disappearance and reappearance" and "unpredictable spatial movements." Conversely, T-GRPO inaccurately attributes the rule to "transformation and disguise," underscoring its inability ground reasoning effectively in the video's 's
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Table Validation on Owen2.5-VL-3B with different frame settings (16/32/64). Video-KTR consistently outperforms Vanilla GRPO, demonstrating robust improvements across benchmarks.
Models
Frames
Video-Holmes
VideoMMMMU
MMVU
TempCompass
VideoMME
Vanilla GRPO
16
32
44.0
58.4
65.2
51.8
Video-KTR
16
33.2
45.1
60.0
67.2
53.0
Vanilla GRPO
32
33.1
44.7
59.5
66.7
54.1
Video-KTR
32
36.2
45.3
61.0
67.9
56.2
Vanilla GRPO
34.1
45.0
60.3
66.8
Video-KTR
36.3
46.8
61.3
68.0
57.5
actual content. This example illustrates the effectiveness of our token-level optimization in aligning model attention with critical visual and temporal signals. Figure 14. Video-KTR further demonstrates its ro- bustness by correctly inferring the underlying character relationship-thie that the two individuals are friends rather than brothers- wweseasw T-GRPO, lacking any supporting evidence, incorrectly selects fraternal re- lationship. In Figure [5] Video-KTR accurately reasons about the character's 's motivation, identifying that the man pretends to dead to escape the woman's attempted murder. contrast, T-GRPO misidentifies the yellow gloves phone call. In Figure [6] Video-KTR successfully tracks the temporal progression in a chaotic chase scene, recognizing that the man first tries retreat into the room, fails, and then relentlessly pursued. T-GRPO, however, mistakenly assumes that the man is chased continuously before attempting to return to the room, thereby misrepresenting the actual event order. These examples demonstrate Video-KTRR enhanced capac- ity for temporal modeling and consistent logical reasoning, allowing it to capture complex event structures with greater reliability.
banana, leading it the wrong conclusion that the was awakened by the woman's s
LLM USAGE STATEMENT
accordance with the ICLR 2026 policy on large language model (LLM) usage, we disclose that an LLM (OpenAl's ChatGPT) was used in limited and non-substantive capacity. Specifically, it assisted with minor language editing (egg, grammar and phrasing), provided suggestions on figure color choices, and offered LaTeX formatting guidance. No research ideas, experimental designs, analyses, substantive scientific writing were generated by the LLM. All scientific contributions and intellectual content are entirely the work of the authors.
18

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Published as conference paper ICLR 2026
What is the core supernatural rule demonstrated by the long-haired woman?
Official Answer
Iynare the limitations of space physics It can raflonly appear in ercloaed speces (auch rriic proving that the dbility penetrate space.
T-GRPO
<thikk-Lat's unalvaz the vikeo step sizp deiemnis supertatand rale denvestrated by the heneehhired wersnn..... 3. Rerasti and Diguise*; The woman is difirrer outfss and settinus, which could indieate that sie las the ability stories where diaracies can change their appearazce
trunsfee and disgaise baself. This comnicn sppenaudd ability farm.
**Ulliiited Stanita Parsure The woran is sees nutcing through the ally and the steet, which seggess that site hes unlimnted
stamina. This is seperationl ahitry that illews her to nin estanded periods withort getting tined
6. "cConral Miectroic Desicad*:; The waman is seen uding a phone, which could indicate that she has the ability control clcetrenic desices. However, this not the superratuon nue decoesstated by her.
7. *fignore the Limiaations Space Plasies The wotann
nasing thouug the allyy and the street, which suppests that has the ability to ignore the limitations space plasies Howevvrr this is not the care supernatural rule densonstrated by her. Based on the analysis, the core euperatioal nile domoontrated the long horred woman her abilary mrassfimm and disqiis
herottf
Flourl on the anrlyds, the coro experatinn mile demonnrrtcd the vongetainn woman is her abilry to mencóm and digriss herelff
Video-KTR(Cen (Tuur))
enalyne
sop dicapprars reappeass
sere superouurll denenctrated long-hatrdd
impites igpore inttations spapee plysks. signifient supensturtl shility allows Bedy uxpeedicably.
""Chopgeencce Repparace***
**Chsing Sccne*": The woman duasing thouga
antronments, including dlleyaas streets possible buman....
emphastzes abiiity to mave quickly unpredictably,
shacing man, by lumpstrnnal
analyeis superational
Figure 13: Qualitative comparison
Anomaly Reasoning. Video-KTR infers the supernatural rule from temporal and visual cues, while T-GRPO produces an incorrect path.
What is the relationship between the man with a black beard and Benjamin?
Official Answer Friend
Beggee esdrea cardnas len offeeny clsse freeding.
sppertive
T-GAPO
the mank aerival and muttione self- quamatine peniol Thes implics persoral minisonhhpp Berperin concomal about mark well-being confirinn that nochal time and that will adfaquammiin for doys. fimbher the man's eiueiims and conamned abou his trali... This sugoois ihe Banjenin expering the man stai: lim, which is cemmen behasier clssc relaiiediins wriltaal un low intenatiors, à cler faat the man with the back hand and Baraminn have close calatonsbip. The test mesagoi and the mank respetss inlistie Bai Benimin contaned sbout the man's well being and is poosidig suppor. which typical is brotherls rdlttimiiig WaTaerefiee. the mot lleely relationsip betwens the man with block beard and Betzenin that bro.bbrss.ssoii Biikkinn clanserr
<thinicslats
vidoo masagy indicats
rdatienniiy Benjenin
of
Video-KTRA (TR(ssss)
with mesage Berjamin
The blick bead blakk beanl
desiiation.
faniliar cach quf-quantiie for
resooés coadhed
living
imply
implics they frieully rchsiondop aisea
the supaadi rdalionlep likdy Benjanis
the lich relatindip
Figure 14: Qualitative comparison on Video- Hae--HooleesSoe Reasoning, Video KTR correctly inferred the underlying character relationship, whereas T-GRPO chose an answer without factual grounding.
19

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Published as conference paper ICLR 2026
What is the root cause of the man suddenly sitting up?
Official Andwer
Playing possum failed The decisive oction of the phone man's ceoliciing implying that hen feigned death was expoded. leading call the polkze.
syrchronos
T-6RPO
Lthini-Lets analyoe... women talkinng the phone, phane and basuna, seess be uiking iccure lots cosside optiotc/uaak wink Wouee up by diggng wemun phons men night thane inlioaiin man was peetending alesp... Given cuntest and the sequence events, meil pázzchli explarazioo up the rinsiur telephose whicee-rr
MIL
appears screen, indicuting
mas, lying down appoas adeep ná.
ais up, sqgaess sometlen uncspected
plazsible option bocasse
been waken up by ringing Suiiin réief This los lieely bocese: liely hazane thene infiosium
the vidax oc:: Playng
waa
Video K KTR(ours)
"ihinic-eff' analyoe.. The vidka bagins with bying the gound, soamingly incomatoos selapp inl.. The talling plone, which could impl she
she commicaaing with semarte... The is bobling lamme and glove which for sumctingg.. woClvvn these descivations the most plasable explanatoon that the was urstending, uncorotions
Lat's exalrato the optioneidd in: Woken ielppore- Them is to intectioon that tolgphon ingltit: Susdenn illeas relef Three indication plusible
untienly
he playing
illroe vcC: Playnn power frial This same
insially and then eatikenly up laD Esaamal impuat cuming There estenal impus shown The eccooluully reaase There inliasiun of Seperattonl
There no indicstor siperatuni forces windter corastenaion, placing and than auidenly
logical
conclssion is
Qualitative comparison on reasoned about the character's motivation, whereas T-GRPO misidentified the yellow gloves as a banana and produced an incorrect answer.
Figure 15:
Temporal Causal Inference. Video-KTK correctly
What correct order of the following events?
Official Answer (----------
orden the pishes the art (35) the red rebe dieappoore (D)
men discovers rade atteng" to escape chased and kolled (10)
T-SRPO
<think-Latss
through This the
dioppean
gint's siggreing ribes, indicating contination namutiv the
seems occur the disppeerance tobes chases man inie excape back event likely iebo..es,uu,, much those eventa can finst then man discovrs the molo, mon nol nines dieppeans, close, fimalyy he tries encape sequeace seans logical... waldfer careful conside the most logical sequence is waweeeaaaasseeee-eeaee
as asionn eagacding that trying option sugaess tuut the giel pushes
tobes actively passung
hous: evant serme
given quiana/ennd (3--2--)---5)
This
Video KTT((our))
"ihin-LLa's trok down the sapuaeo of cvmns as desniital in the viaarieinl The gid puchos cant thoouh the etrett This is the fini event memioned lnz. The mas in ned moses disappors for the diss niee This happens after the gri pushes the cart vni. The mon dicovers an shandoned mdlo. This evere fallows the diopperence of the mon
mber ind. The mon in red rites chouss with
kaife. This happzrs after the man discoves the radio ud, The man sies ssuae back into the house This is the final event
menioond, in stw,ow lefs match tais sequence with the given gn ① 2 D This option does noe match
segaeuuc/aß: (DD-22-3 3-0) This uplin maiche Stindo Sammees seguesee... The oeder efeeess in 1--0-2-9440
In 2-3-5-0-3 This spicon does natch the
Figure 16: Qualitative comparison on Video-Holmes-TT Timellne Analysis. Video-KTH correctly identified the sequence of events, whereas T-GRPO confused the temporal order during the chaotic chase.
20


Reviews:
[{"review_id": "glciyiQt61", "reviewer": "Reviewer_DmeU", "summary": "This paper focuses on video reasoning with multimodal large language models. Existing methods rely on coarse sequence level rewards or single-factor token selection for reinforcement learning (RL). This work Video-KTR is a policy shaping framework for token-level RL that only look at three types of tokens, which are visual-aware tokens (i.e., tokens closely associated with visual input like appear, show, etc.), temporal-aware tokens (i.e., tokens sensitive to the temporal structure of videos like finally, first, etc.), and entropy tokens (i.e.,  reasoning-critical tokens like however, wait, seem, now, etc.). Therefore, Video-KTR learns semantically informative, modality-sensitive, and filters low-value tokens, which contributing to the strong performance across five benchmarks.", "questions": "Is the Video-KTR model initialized from Qwen2.5-VL? It's better to make clear about the base model for implementation in the paper.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The general idea is simple and insightful by letting the model reinforce on 3 types of tokens that are crucial to video reasoning, but effective that has performance gain over 5 video reasoning benchmarks.\n2. Clear ablation study of all combination of 3 types of tokens to show clearly what type of token matters the most and how each type of tokens contribute to the performance gain.\n3. 7 research questions are insightful. For examples, by using the same dataset and training recipe, they made sure that the comparisons are fair. There are linguistic insights like visual-aware tokens are mainly nouns, temporal aware tokens emphasize verbs and pronouns, and entropy-aware tokens has a higher share of adjectives. A general insight for large language models is that the log-probability differences is a reliable and efficient signal for tracking prediction-confidence shifts.\n4. Writing and images are clear and easy to understand.", "weaknesses": "1. The performance gain over the Vanilla GRPO for three types of tokens, despite being higher, but it's a small increase. When enabled all three types, the average delta performance gain is just 2.4% for the average of three benchmark, while other ablations show even smaller differences. Therefore, the effectiveness of the Video-KTR is limited from the results.", "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": "RDRRmzvdHR", "reviewer": "Reviewer_DCwf", "summary": "This paper proposes a way to select important tokens for reinforcement training visual LLMs to enhance their reasoning abilities. The method includes identifying visual-aware, temporal-aware and entropy-aware tokens (connecting words or logical words in my opinion) and only compute the loss based on those selected tokens.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The paper explores which tokens are more important to be optimized to enhance the reasoning process. This is a cheap and useful technique in general.\n\n2. The paper is well-structured and clearly written, showing the background, proposed methods and experimental setup clearly.\n\n3. The results seem promising as the token selection method yields better performance on reasoning-centric tasks such as video-Holmes. Ablation studies and various plots were made to support the findings.", "weaknesses": "1. I am a bit concerned regarding the theoretical foundation of this work. What is the actual contribution of the tokens that has actually been masked? For example, would these tokens introduce unwanted noise to the gradient w.r.t. the logits. A theoretical derivation by looking at the actual influence with or without the mask in the loss when taking the gradient w.r.t. the logits or even deeper in the network should be provided. __Moreover__, tokens that are visual-aware or temporal-aware, does not necessarily mean they need to be __optimized__ unless they really produces differences when sampling multiple paths. For example, I can have \"cat\" as a token that is visual-aware, but that cat appears in every samples I draw, and hence not producing useful information and can be excluded here. In my opinion, the tokens that should be optimized are the tokens that carry __information inconsistency__ across samples. I find this part of justification missing in the current manuscript.\n\n2. I am also concerned about the entropy-aware tokens. I feel that the authors are trying to introduce the concept of information inconsistency here and to optimize tokens where the model is highly uncertain. However, I am not sure if entropy is a good metric, especially looking at the range of values it can get, a threshold might be difficult to draw, because different value on different tokens mean different things (even if you select via top-r% they are still inconsistent across reasoning paths). I find this part quite counter-intuitive and would leave this to other reviewers to decide.\n\nOverall, I am not fully convinced by the proposed methodology, given that nowadays many factors may influence the model performance. Deeper analysis and theoretical justifications are needed for the current manuscript to be reasonably sound for publication.", "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": "ZCninUFcPR", "reviewer": "Reviewer_UDPQ", "summary": "(I tend to write shorter reviews and the length of the review does not reflect the quality of paper or the time spend on reviewing it). \n\nNote that I am not an expert in Video modelling nor Reinforcement Learning and the review is from a perspective of a general Deep Learning researcher. \n\nThis paper proposes a few heuristics to improve the RL training of reasoning models (especially in the context of Video) by using things like perceptual dependence, remporal sensitivity and entropy based uncertainity. The authors show that each of these heuristics reveal natural points to not have RL be updating all the tokens in the trajectory but only a handful. They paper's experiments are strong and have extensive analysis. The writing is very easy to follow.", "questions": "See above. Again I am not an expert so will defer to other reviewers on this paper.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "Clear explainations and good experiments. The extent of empirical investigations is commendable -- I am not an expert so can not comment on the strength of the experiments.", "weaknesses": "1) The comparision to the closed source models is from almost 2 generations ago and would be good to have latest to have a better understanding of where things stand. \n\n2) While the heursitics are working great for this task at hand, given the past of DL and my experience, the heuristics stop helping in general purpose cases and while we scale up. It is a strong result for now in a narrow domain but a broader investigation is what will concretize the proposed things in the modern RL pipelines. I strongly suggest either doing a more general purpose evaluation to support this broadly.", "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)