LongPerceptualThoughts: Distilling System-2 Reasoning  for System-1 Perception
Authors: Yuan-Hong Liao
Sven Elflein
Liu He
Laura Leal-Taixé
Yejin Choi
Sanja Fidler
David Acuna
Venue: COLM 2025 (2025)
Keywords: multimodal reasoning
vision-language models
chain-of-thought
TL;DR: We introduce a novel data generation pipeline for generating multimodal reasoning data by injecting key cognitive behaviours.

Abstract: Recent reasoning models through test-time scaling have demonstrated that long chain-of-thoughts can unlock substantial performance boosts in hard reasoning tasks such as math and code. However, the benefit of such long thoughts for system-2 reasoning is relatively less explored in other domains such as perceptual tasks where shallower, system-1 reasoning seems sufficient. In this paper, we introduce $\textit{LongPerceptualThoughts}$, a new synthetic dataset with 30K long-thought traces for perceptual tasks. The key challenges in synthesizing elaborate reasoning thoughts for perceptual tasks are that off-the-shelf models are not yet equipped with such thinking behavior and that it is not straightforward to build a reliable process verifier for perceptual tasks. Thus, we propose a novel three-stage data synthesis framework that first synthesizes verifiable multiple-choice questions from dense image descriptions, then distills simple CoTs from VLMs for those verifiable problems, and finally expands those simple thoughts to elaborate long thoughts via frontier reasoning models. In controlled experiments with a strong instruction-tuned 7B model, we demonstrate notable improvements over existing visual reasoning data-generation methods. Our model, trained on the generated dataset, achieves an average +3.4 points improvement over 5 vision-centric benchmarks, including +11.8 points on V$^*$ Bench. Notably, despite being tuned for vision tasks, it also improves performance on the challenging text reasoning benchmark, MMLU-Pro, by +2 points.

Published as a conference paper at COLM 2025
Long PereeptualTTouughs:: Distilling System-2 Reasoning for System-1 Perception
Yuan-Hong Liao
Sven Elflein
Liu He*
Laura Leal-Taixé
Yejin Choi ?
Sanja Fidler
David Acuna
Abstract
Recent reasoning models through test-time scaling have demonstrated that long oftho-hs- can unlock substantial performance boosts in hard reasoning tasks such as math and code. However, the benefit of such long thoughts for system-2 reasoning is relatively less explored in other domains such as perceptual tasks where shallower, system-1 reasoning seems suffi- SeaePercetuuaThougeeee a new synthetic
cient. In this paper, we introduce dataset with 30K long-thought traces for perceptual tasks. The key chal- lenges in synthesizing elaborate reasoning thoughts for perceptual tasks are that off-the-shelf models are not yet equipped with such thinking be- havior and that it is not straightforward to build reliable process verifier for perceptual tasks. Thus, we propose a novel three-stage data synthesis framework that first synthesizes verifiable multiple-choice questions from dense image descriptions, then extracts simple CoTs from VLMs for those verifiable problems, and finally expands those simple thoughts to elaborate long thoughts via frontier reasoning models. controlled experiments with strong instruction-tuned 7B model, we demonstrate notable im- provements over existing visual reasoning data-generation methods. Our model, trained on the generated dataset, achieves an average +3.4 points improvement over 5 vision-centric benchmarks, including +11.8 points on V Bench. Notably, despite being tuned for vision tasks, it also improves performance on the text reasoning benchmark, MMLU-Pro, by +2 points." I
1 Introduction
Reasoning models, such as OpenAl's ol (OpenAI et al., 2024) and Deepseek's R1 (DeepSeek- Al al., 2025), have demonstrated remarkable capabilities in solving complex reasoning problems by scaling test-time compute. Intuitively, they increase the number of tokens generated at inference-time, allowing the model to "think longer" producing longer chain- of-thoughts (CoTss) that go beyond typical linear rationales that mimic textbook responses.
However, despite numerous attempts to match the performance of models like ol or R1 on challenging math benchmarks (MAA, 2024; Lightman et al., 2023), less effort has been directed toward tasks beyond mathematical reasoning. Most notably, how to generate synthetic, long-form CoT reasoning traces that solve vision-centric suikb-suitabl for both supervised fine-tuning and preference-based RI-remains largely underexplored.
Vision-centric tasks have proven challenging for vision-language models (VLMs), especially when the tasks require object counting and localization, scene understanding, and 2D/3D spatial reasoning (Liao et al., 2024; Rahmanzadehggrrii et al., 2024; Campbell et al., 2024). Prior works have addressed these challenges by helping VLMs "see" better. Common approaches include modifying the input image (e.g., through cropping) or incorporating intermediate representations, into the CoT (Wu & Xie, 2024; Shao et al., 2024a; Wu al.,
Project website: https: http:///ddewwwool// github. he/LooggeerreptuTToohhhhh
University of Toronto, Vector Institute, NVIIDA,, PPurdue University
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Standard (Short) CoT
By examining on the pamphlet, see that reeds "SNOW" at the top. The answer is (A)
LongPerceptuulThought (Ours)
Hmm, but wait, let me double check with the details. There's also word "PUSH-BUTTON on the top of the pamphlet. So the top part has both worcss.
Verification:
Checking intermediate results
BUTTOR
Betlial
But the question specifically asks the top. Since PUSH-BUTTON on the very that must be the primary answer.
SNOW
Subgoal setting: Break down question
However, "SNOW" written in big red center, mayhe that's more prominent. Wait, the question is about the top, so think "PUSH- BUTTON" the top, making the correct answer. The
Backtracking:
Explicit ravision when errors are detected
Question:
What written at the top of the green pemphlet?
(B) PUSH-BUTTON BUTTON (D)
Figure 1: Longserreptual/Touutts is a new synthetic dataset with 30K long-thought traces for vision-centric tasks. Each trace contains diverse cognitive behaviors (e.g., ver- ification, subgoal setting, and backtracking), akin to system-2 reasoning. CoTT generated by open-source VLMs often produce linear, rigid reasoning traces (top). In contrast, our novel data synthesis framework effectively expands these simple thoughts using frontier reasoning models, equipping VLMs with complex reasoning structures and rich cognitive behaviors-effectee distilling system-2 reasoning into instruction-tured VLMs.
2025b). In contrast, we propose to synthesize data that implicitly equips VLMs with an internal search mechanism-org one that unfolds through a textual inner monologue, enabling the model to explore multiple potential solution paths: revisiting different image regions, verifying intermediate conclusions, identifying inconsistencies, and self-correcting when necessary. Our approach is complementary to prior methods and mirrors the behavior observed in reasoning models like R1 and commercial VLMs such as ol,, where reasoning performance improves by scaling test-time inference. We emphasize we do not claim that long textual CoTs are inherently superior or the only way to scale test-time inference in VLMs. Rather, our goal is to synthesize data to equip models with such capability-ai approach shown to be effective in SoTA reasoning models. Furthermore, given the difficulty of building reliable process verifiers for search in perceptual tasks, our data-centric method offers a practical alternative.
In this work, we take first step toward scalable method that synthesizes long CoT data for vision-centric tasks. Specifically, we propose a novel three-stage data synthesis framework that: (1) generates synthefic verifiable multiple-choice questions from dense image captions, (2) extracts simple CoTs from VLMs for those questions, and (3) expands these simple CoTs into richer, long-form reasoning traces using frontier reasoning models. Notably, our framework performs three layers of synthesis: one to generate questions, one to think, and the last one to think harder. As shown Fig. 1, using our framework, we generate weeeeerreuuaaahhooeeeeee
dataset of 30k examples for both supervised fine-tuning (SFT) and direct preference optimization (DPO), and use it to fine-tune a strong instruction-tuned VLM. The resulting model shows an average +3,4 points improvement across 5 vision- centric benchmarks, including a gain of +11.8 points on V Bench, while typical multimodal reasoning datasets fail to improve the base VLM due to overthinking. Notably, despite being tuned for vision tasks, also improves on the challenging text reasoning benchmark MML.UPro by +2 points.
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2 Synthesize Long CoT Data for Vision-Centric Tasks
In this section, we introduce novel data synthesis framework to synthesize long chain-of- thought (CoT) data for fine-tuning a vision-language model (VLM), Inspired by DeepSeek's RI, we are interested in collecting data consisting of thoughts and answers in the format of <think> thought </think> <answer> answer </answer>. We start by discussing two desired properties of reasoning data for vision-centric tasks in Sec. 2.1. Based on these two properties, we explain our data synthesis framework in Sec. 2.3. Finally, we use the synthesized long CoT data to construct LongPerreptualThought consisting of both SFT and preference datasets in Sec. 2.4.
2.1 Desired Properties in Long Chain-of-Thought
Inspired by the recent success in OpenAT's ol and DeepSeeks's R1, we further define Long CoT as an extended, structured rationale that mirrors how human might approach complex visual reasoning tasks. Unlike the short, linear responses typically produced by current open-source VLMs, Long CoTs explore alternative solutions, verifying intermediate steps, and adjusting course when necessary. Drawing on the framework proposed in Gandhi et al. (2025), we characterize Long CoTs in vision-centric tasks through three core cognitive behaviors: verification (checking intermediate conclusions for correctness), backtracking (recognizing and revising failed solution paths), and subgoal setting (breaking down the task into smaller, solvable components). These cognitive behaviors have been observed in LLM to increase performance by scaling test-time compute (Muennighoff et al., 2025).
To study cognitive behaviors in vision-centric tasks, we begin by analyzing the outputs of strong instruction-tuned VLMs, following Gandhi et al. (2025). Despite its general capabili- ties, the model rarely displays the cognitive behaviors described earlier. The responses are often shallow and rigid, lacking the iterative, self-corrective reasoning we aim to capture. Figure 3a quantifies this gap between the response from -wwen..--V.-7B-Instee ini 2.0 Flash Thinking. At the end of this section, we introduce SeePPerreallhouggeeee that drastically diversifies the standard CoT with the desired cognitive behaviors.
and Gem-
2.2 Preliminaries
Formally, given an image v, our goal is to construct a quadruple (v,q, a,), consisting of question q, a long CoT reasoning trace Z, and final answer a. We also assume the access to dense image descriptions A long CoT is composed of multiple thoughts that incorporate cognitive behaviors such as backtracking, verification, and subgoal setting. Formally, we define long CoT as a sequence of intermediate thoughts: Z == 71 + 72 t ..., where + denotes concatenation and a sequence of sentences, typically delineated by double new lines, i.e. nn").".
For preference data in reinforcement learning-ov goal is to construct a preference pair of (v,q, Z a > (v,q.Z-,a ) where the superscripts + and indicate the pre- ferred (correct) and non-preferred (incorrect or suboptimal) reasoning trajectories and their answers, and > denotes that the left-hand tuple is preferred over the right-hand one.
2.3 Thought-Expansion Distilling System-2 Reasoning into Instruction-Tumn VLMs
For an image v, we begin by assuming access to its dense image description e that provides comprehensive visual features in the image. While in principle, one could also obtain such descriptions using a captioning model, here we assume access to such a dataset egg,, DOCCI (Onoe et al., 2024) or DCI (Urbanek et al., 2023). In proposed data synthesis framework, we leverage three foundation models: an LLM, a VLM that takes interleaved image and text as input and generates text, and reasoning LLM that explicitly produces thoughts and answers. We use MLLM, MyLM, and M Reason to denote them, respectively.
Below, we describe the three key stages of our data synthesis process.
Stage 1: Convert dense descriptions to multiple-choice questions We first convert dense descriptions into multiple-choice questions (MCQs) using LLM. Specificall we prompt
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Stage Ask: Convert Dense Captions to Multiple-choice Questions with LLM
Dense Caption:
Multiple-choice Question:
The feminine mannequin is wearing aqua shiny high healed shoes...
What type of shoes is the feminine mannequin wearing?
(A) Aqua Shiny High Hoels (B) Yellow Sneakers
Stage 2 Think: Extract Simple CoT from VLM
Simple CoT:
To determine what types of shoes The answer is (B)
Stage 3 Think harder: Pre-condition, then Extend
thinking with Reasoning LLM
User: Dense Caption
Multiple-choice Question
Assistant: <think>
Simple CoT
Wait,
Extended thinking:
might be wrong. Let's double check, the mannequin wearing yellow pattern slacks, The answer (A)
Figure Ask, Think, and Think Harder:
The three stages to synthesize long CoT data for image and its associated dense caption, we
vision-centric tasks. Assuming the access first ask an LLM to convert dense captions to multiple-choice questions. In Stage 2, we extract simple CoT from VLM. These simple Colss typically exhibits shallow and rigid reasoning, especially in vision-centric tasks. Therefore, in Stage 3, we precondition a reasoning LLM with these simple CoTs and append a subtle cue, e.g., "Wait,", to elicit more diverse long CoTss.
MLLM to generate MCQs based on an image and its associated dense descriptions. This step offers two key advantages that are leveraged in subsequent stages: (1) It ensures that each generated question is answerable using only the dense descriptions, allowing us synthesize the reasoning process purely from the text modality. (2) The multiple- choice format enables easy identification of prediction correctness, which is essential for constructing positive and negative pairs in our preference dataset. Formally, this step produces triplet (v, a²)a))))))))))))) == MLIM(v,c). We use gpt-4o-mini as MLLM to balance the cost and the quality of MCQs.
Extract Simple CoTTs from VLM To generate long CoTs that the VLM is familiar with, we use the same VLM that will later be fine-tuned. Specifically, we prompt MyIM with the image and the multiple-choice question generated in Stage 1 to produce rationale and a final prediction, denoted as an) := MMLM v, (v,q). Sampling from the same VLM ensures that the synthesized CoTs remain within the model's output distribution, which we observed to be a key factor downstream performance. By comparing the predicted answer aj with the ground-trute answer a* from Stage 1, we can further categorize the data into positive (z a, or negative examples (z a, These can then be reused to construct either SFT or preference dataset. This process is akin to the rejection sampling in self- training algorithms such as RFT (Yuan et al., 2023) and STaR (Zelikman et al., 2022). We choose Siven2.--VL-77-Imme non-trivial probability of exhibiting cognitive behaviors (Gandhi al., 2025).
Stage 2:
as our MVLM as the Qwen2.5 series has demonstrated
Stage 3: Thought-Expansion using the Reasoning Model. The analysis in Fig. 3a reveals that CoTs sampled from open-source VLMs typically exhibit shallow and rigid reasoning, with limited exploration in the output space. Inspired by the diverse cognitive behaviors observed in the responses of frontier reasoning models, we aim to leverage a reasoning LLM to generate long CoTs. However, naively sampling from MReason can produce CoTs that deviate significantly from the output distribution in VLM, which may degrade the performance of instruction-tuned models during fine-tuning. The similar findings have

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0.5
Gamini Thincrr tnggheeetaaahaggtt iDunt)
X10-
Cura (SF7)
Riaa Countin OUT
04
122
Lenphrosteallla (Bac)
Overral Overral Performanee
Derect anoper
CXT
03
1,00
0.5
Vigg
150
4:50
VUA derking
6.02
Types
Behaviors
302
200
420
600
700
101
#uttput Toleens (log)
10
(a) Cognitive behaviors in CoTs
(b) Length of CoTs
(c) SFT/DPO performances
Figure 3: (a) Analysis of Cognitive Behaviors in Chain-of-Thought (CoT). CoTs from open-source VLMs often follow rigid structures. In contrast, frontier reasoning VLMs-such as Gemini 2.0 Flash Thinking exhibbi more diverse cognitive behaviors, including subgoal setting, backtracking, and verification. Our introd long CoT dataset, LongPerceplle alThoughts, also demonstrates wide range of such behaviors. (b) Length of CoTs. The CoTs in LongPereeptul/Thoughe are significantly longer than those generated by popular VLMs, e.g. Qwen2.5-VL. (c) Response length vs. aggregated performances. Fine-tuning VLM on LonglerceepuullThoghh with complex reasoning structures lead to higher overall performances with slightly more output tokens. On the other hand, fine-tuning on other multimodal reasoning leads to over-thinke thinking and worse performance. Cognitive behaviors are quantified following Gandhi et al. (2025).
been discovered in LLMs well (Ren et al., 2024; Li et al., 2025; Wu et al., 2025a). To address this, we introduce a thought-expansion mechanism that guides the reasoning LLM MReason to extend the CoT produced in Stage 2, while injecting cognitive behaviors such as backtracking, verification, and subgoal setting. Specifically, we precondition M MReason with the CoT generated by VLM 71 and append a subtle cue- selected from set of predefined markers m (eg., "Wait," "Hmm," "Altenativelly") " elicit more reflective or exploratory responses. Formally, we structure the prompt as:
User: c c
Assistant: <think Z1 m
and ask MResson to continue the thought to obtain (22, This approach enables the reasoning LLM to expand the familiar reasoning traces while enriching them with non- linear problem-solvingg behaviors. Similar to Stage 2, we can also use d2 to categorize the data into positive or negative examples. Fig. 2 demonstrates the way to construct such prompt visually. We use DeepSeek-Ri-Disile 1etl-Disill--wee-3222 as our MResonn as it is derived from the same Qwen2.5 series as the MyLM- For more details, see the prompt template in Appendix F.7
Our proposed framework is scalable and only assumes the access to dense image description datasets. From Stage 1 to Stage 3, for an image and its associated descriptions c, we obtain MCQs (q,a*) in Stage 1. Then, in Stage 2 and 3, we obtain two intermediate thoughts and their associated predicted answers (21, Z2, a1, a2). Finally, we have long CoT data Z obtained by: 71 + m + 72. We will omit m in the following sections for the sake of brevity and clarity.
2.4 Construct SFT and DPO Datasets
In Sec. 2.3, described a process to obtain long-form CoT's that not only aligns with the VLM's output distribution but also contains system-2 reasoning behaviors. To construct supervised fine-tuning (SFT) dataset, we collect CoTs that lead to correct predictions. This includes examples of the form:
(zi (zi + 2 (ZT 1 スミ a²)
To construct a preference dataset, we follow Setlur et al. (2024); Zhang et al. (2025) and define pairwise preferences based on correctness and compactness Team al. (2025). Specifically:
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Correctness:
az) - (2) ( @ 02) - (7 a
Compactness:
az)) + a2)
(z)
Akin to Setlur et al. (2024), by constructing the preference pairs of (z, 2 a2)) > (2,,a) encourage the model to increase the likelihood P(zž a [ and decrease the likelihood P(a, | leading better credit assignment.
Filtering. Since Z2 is generated by reasoning LLM using dense captions as input, it may include phrases like "As the description says." " To address this, we define list of "bad words" and filter out any thoughts containing them.
Details of LongPereeptul/Toougts We use 500 images and their dense captions from DOCCI. Stage produces 4590 multiple-choice questions (MCQs). For long CoT data, we construct an SFT dataset with 30295 examples and preference dataset with 17208 pairs, following filtering and deduplication. We use gpt-4o-mini as MLLM, Qwen2.5-VL- 7B-Instruct as MyLM, and RL-Distill-Wwen-B00 as MReason-
Experiments
In this section, we first describe the experimental setup on five vision-centric benchmarks in Sec. 3.1 and present our main results in Sec. 3.2. In Sec. 3.3, we go beyond vision by evaluating our fine-tuned VLMs on a challenging text-only benchmark. Lastly, in Sec. 3.4, we analyze the response of the fine-tuned VLMs.
3.1 Setup
Model. We use Qwen2.5-VV-7B-Insttre (Bai et al., 2025) as our base model to fine-tune throughout the paper. For the sake of brevity, we refer to it as BaseModel in this section. We adopt full-parameter fine-tuning using LLaMA-factory (Zheng et al., 2024). See more training details in Appendix F.
Benchmarks. We evaluate our models on vision-centric tasks. For benchmarks covering general knowledge, only keep their vision-centric splits, such MME-RealWorld (Zhang et al., 2024) and MMStar (Onoe et al., 2024). To better clarify the differences, we refer to them MME-RW-V and MMStar-V, respectively. Additionally, following Tong et al. (2024a), we include the vision-centric benchmarks: CV-bench, V Bench, and MMVP, that involve 2D/3D spatial reasoning, fine-trained attribution, coarse scene understanding, etc. In total, the benchmarks consist of 10284 images and 15315 questions. More details are in Appendix B.
Evaluation metrics. All the benchmarks used in this work are in multiple-choice question format. We standardize their format and use regex to extract the answers. We report accuracy across all benchmarks.
Baselines. To explore the vision-centric capabilities of BaseModel, we evaluate its zero-shot predictions and apply a prompt-based thain-of-thought approach. Specifically, we prompt the model to generate <think> thought </think> before producing an answer-a method we refer to as Internal Thinking COT.
For multimodal datasets, we compare LongPerceptualThoghhee with one captioning dataset, DOCCI, and two multimodal reasoning datasets, Virgo (Du et al., 2025) and VAAL- thinking (Chen et al., 2025). (1) DOCCI is human-annotated dense caption dataset, high- lighting comprehensive descriptions for images. For a fair comparison with LongPerceptu- afThoughts, the exact same set of 500 images and their captions training data. (2) Virgo distills reasoning capabilities from the language model QwQ (Team, 2024b) and the multimodal model QvQ (Team, 2024a). We adopt Virgo's self-distillation split, generated

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Approach
Avg
CV-Bench
V" Bench
MMVP
MMStar-
MME-RW-V
Swen225-VL-7--nnmmmcc
58.47
74.74
48.51
73.67
63.73
31.68
+ Internal Thinking CoT
59.18
75.42
55.08
70.60
62.40
32.40
+ DOCCI
36.14
50.82
39,96
48.67
8.67
32.58
VLAA-thinking
42.32
68.50
53.53
66.67
0.53
22.38
Virgo
50.87
67.22
44.14
57.67
57.60
27.71
LongPenoephua/Thoghht SFT (Ours)
59.90
76.05
60.53
(+1210)
70.00
60.67
32.25
LonggPerootuullhouggss SFT DPO (Ours)
(-5-4)
76.61 (-1.8)
60.31
(+11.))
75.00 (+1:3))
64.00 (40.27)
(+177)
Table 1: Main results on five vision-centric benchmarks. We group the approaches into three categories: training-free methods, existing multimodal reasoning datasets, and our pro- posed LongPerceptualThogge
On vision-centric tasks, fine-tuning on other multimodal reasoning datasets often leads to poorer performance, likely due to reduced instruction- following ability, domain mismatch, or an inability capture the complex reasoning learned by larger models. In contrast, fine-tuning on improvement of +1.5 points, and this gain increases to +3.4 points when using preference pairs. Notably, it achieves 12-point improvement on the challenging V Bench.
yields an average
by first distilling QwQ into Qwen2-VV--2B-Innttrue then using the fine-tuned model to produce reasoning data for multimodal questions. The Virgo dataset includes 14,540 540 exam- ples. (3) VLAA-thinking generates multimodal reasoning data by prompting DeepSeek's s R1 model with additional caption information. It contains 158k examples, from which we randomly sample 25k for training to match a similar size to our dataset."
3.2 Main Results
We report aggregated performances in Table 1 and detail our main findings on five vision- centric benchmarks:
LongsPereptualThoughts consistently improves performance on vision-centric bench- marks by +3.4 points via DPO. We first perform supervised fine-tuning on the synthesized LongPerrettualThoughts. Across 5 benchmarks, we improve BaseModel by nearly +1.5 points on average, especially in challenging tasks such as V* bench, improving by +12 points. However, the improvements on the rest of the benchmarks are marginal. We hy- pothesize that this is due to noisy or erroneous tokens in our SFT datasets, which may hurt fine-tuning performance. While several prior works suggest the impacts of such erro- neous tokens are marginal, they either work on models under 300M parameters (Ye et al., 2024) or target different aspects, such as safety alignment (Zhang al., 2025). In this work, we try not to over-engineer the training recipe to highlight the effectiveness of the syn- thesized datasets. Unlike VLAA--hinkkig and Virgo that simply distill knowledge from reasoning I.MSs or VLMs, our data generation pipeline allows us to construct preference data. By fine-tuning on these preference pairs, the aforementioned erroneous tokens might naturally be mitigated. For example, by performing
seeference-based fine-tuning such as [2,,,] the model should naturally increase the likelihood of P(z) a |z, as opposed to P(a, 1z, ). This helps the model reduce the impact of erroneous tokens. We find that by first applying SFT and then DPO, we obtain consistent improvements across all evaluation datasets, improving by +3.4 accuracy points. Overall, we find that LongPerceptualTTie Thoughhss generally leads to consistent improvements and the preference data is the key to bring up the improvements.
DPO, on (z + 2 az
Off-the-shelf captioning data hurts instruction-tuned VLMs on vision-centric bench- marks. Since SegPPerretualThoughhe is derived from DOCCI, we are interested to see if fine-tuning BaseModel on DOCCI improves. Table shows that training DOCCI re- sults in inferior performances. Perhaps expected, we find that fine-tuning on DOCCI alone especially leads to bad instruction following.
off-the-shelf distillation hurts performance on vision-centric benchmarks. Both Virgo and VLAA VLAA-thinking are multimodal reasoning datasets. VLAA-thinking is distilled from
We accessed the dataset in mid-March 2025.
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R1 with the help of image captions. Virgo is distilled from fine-tuned VLM distilled from QwQ. While both datasets are equipped with complex reasoning structures, fine- tuning the BaseModel does not improve vision-centric performance; instead, finetuning VLAA-thinking and Virgo hurts the performances by -16.15 and -7.6 points, respectively. We hypothesize that there are two reasons that lead to the performance drops: (1) Both datasets are distilled from a much much larger LLMs (671B RI model) or VLMs (Qwen2-VL- 72B-Instruct), potentially resulting in the learnability gap (Li et al., 2025). (2) In particular, the multimodal reasoning data from Virgo is math-focused. We hypothesize that there is gap between reasoning over perceptual tasks and math-related tasks. On the other hand, VLAA-thinking consists of diverse set of datasets including DocVQA (Mathew et al., 2021), ChartVQA (Masry al., 2022), elc. When using reasoning data exclusively from more natural image sources, we surprisingly observe worse performance than random subsampling. See Appendix for details.
3.3 Beyond Vision: Evaluation the Text-Only Reasoning Benchmark
Following the same setup in Sec. 3.1, we evaluates VLMs fine-tuned on multimodal reason- ing training datasets in out-of-ciitributiio (OOD) tasks. particular, we adopt MMLU-Pro, a challenging text-only reasoning benchmark.
MMLU-Pro (Wang al., 2024a), MMLU-Pro built on top of MMLU (Hendrycks et al., 2021) by inte- questions and ex- panding the choices set. MMLU-Pro spans 14 diverse domains including mathematics, physics, chemistry, etc., encompassing over 12000 questions.
Approach
Acc
grating more reasoning-
Qwen2.5-VL--7-nnsttuue + CoT
48.07
DOCCI
32.99
VLAA-thinking
21.56
Results. As shown in Table. 2, we find that BaseModel fine-tuned on Longeerceptual Thoughts surprisingly improves on these text-only reasoning tasks, with average gain of +2 points. In contrast, VLAA- thinking and Virgo hurt performance, suggesting that directly distilling from stronger teachers may lead to sharp drops in OOD tasks. We propose two effective- ness: (1) it introduces complex reasoning structures that improve BaseModel 's general reasoning abilities; and (2) it remains close to the original output distribution, making the new reasoning skills easier to learn without disrupting existing knowledge. Additional MMLU-Pro evaluation details are provided in Appendix
Virgo
37.95
+ Ours SFT Ours SFT + DPO
50.77
50.20
Table 2: Evaluation on out-of- distribution tasks text-only reason- ing benchmark MMLU-Pro.
hypotheses for LongPerceptualThougee
3.4 Analysis Fine-tuned VLM Responses
To better understand our fine-tuned VLM, we visualize its responses with two key factors: aggregated performances and question difficulties.
Response length vs. performances. There has been growing interest in how LLMs leverage test-time compute. To investigate this, we aggregated response lengths and performance across five vision-centric benchmarks. Fig. 3c illustrates the relationship between test- time compute-measuree by response length-and model performance. We observe that VLMs fine-tuned on especially after SFT. Interestingly, DPO training results in shorter responses, which aligns with the compactness encouraged during DPO pair construction. One possible direction is to exclude such preference pairs to allow models to make fuller use of test-time compute. In contrast, other multimodal reasoning benchmarks reveal signs of overthinking, where models generate unnecessarily lengthy responses.
tend to produce slightly longer responses,
Response length vs. question difficulty. Another desirable characteristic of the thinking process in LLMs is their ability to allocate appropriate "thinking time" based on a question's 's difficulty. Following prior works (Lightman et al., 2024; Snell et al., 2025), we define question difficulty with respect to a base VLM, i.e., Qwen2.5-VL-2 ---B---nstttut For each question, we estimate the model's accuracy using 11 samples and bin the questions into two quantiles:
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easy and hard. Our analysis focuses on the outputs of the VLM fine-tuned via DPO LongPereptuullhoughte We observe that the model naturally allocates more test-time cempute-reflectee in longer responses-for for harder questions, where its original (pre-fine- tuning) performance was worse. See Appendix C, for details and visualization.
4 Related Work
Reasoning in Large Language Models. The complex reasoning abilities of large language models (LLMs) have been uncovered through various approaches. Chain-of-thought (Col) prompting elicits their intrinsic reasoning capabilities, improving performance on language- based causal reasoning tasks (Wei et al., 2022), and has been extended into tree-based searches enhance reasoning further (Yao et al., 2023). Similar search-like behavior can be induced through verifier guidance (Lifshitz et al., 2025), curated datasets (Shao et al., 2024b), or supervised fine-tuning on synthetic reasoning data (Gandhi et al., 2024; Lehnert et al., 2024). More recently, DeepSeek-R1 (Guo et al., 2025) achieved state-of-the-art reasoning through reinforcement learning, exhibiting human-like traits such as self-correction and verification. Other open-source efforts have been explored to study various tricks to scale reinforcement learning Liu et al. (2025a); Pan et al. (2025). In contrast, sl (Muennighoff et al., 2025) and LIMO (Ye et al., 2025) improves mathematical reasoning via supervised fine-tuning on less than 1000 distilled reasoning traces. While most prior work focuses on math and coding tasks, our goal is to explore how such strong reasoning capabilities can be effectively applied to perception.
Reasoning in Vision-Centric Tasks. Unlike reasoning in math or coding tasks, vision-centric problems often involve significant uncertainty due to partial information, perceptual noise, and visual ambiguities. Prior works primarily address this by helping VLMs "see" better. For instance, SEAL (Wu & Xie, 2024) uses search-like cropping mechanism to iteratively navigate an image, while VisualCoT (Shao et al., 2024a) generates auxiliary visual cues to guide attention. Other approaches (Wang et al., 2024b; Liao et al., 2025) decompose complex tasks into simpler verification steps to enhance model robustness. In contrast, we aim to teach VLMs to reason better encouraging them to explore multiple solution paths by revisiting image regions, verifying intermediate conclusions, and engaging in textual inner monologue. Concurrent work on multimodal reasoning addresses this challenge, particularly in math problem solving, using techniques such as distillation from advanced reasoning LLMs (Du et al., 2025; Thawakar et al., 2025) and reinforcement learning (Liu et al., 2025b; Huang et al., 2025). In this work, we study how system-2 reasoning can improve vision-centric tasks, and propose a data synthesis framework that generates long CoT examples to teach visual reasoning through deliberate, step-by-step thinking in the textual space.
Conclusions
In this work, we explore how system-2 reasoning can enhance vision-centric tasks. We intro- duce a novel, scalable data synthesis framework that requires only dense image captions. The framework generates verifiable multiple choice questions, extracts simple chains of thought (CoTs) from vision-language models (VLMs), and expands them into rich, long- form reasoning traces using frontier reasoning models. This process yields alThoughts, synthetic dataset containing 30k detailed reasoning traces for perceptual tasks. Fine-tuning Diven2.5-VL-7B-Ineee
on TnnPPerrptualThoughhh by +3.4 points across five vision benchmarks, including an -11.8-point gain on V* Bench. Remarkably, despite being trained on vision tasks, the model also improves by +2 points on the out-of-distributio text-only reasoning benchmark MMLU-Pro.
improves performance
Acknowledgements
We thank Rafid Mahmood, Jaehun Jung, Jen-Hao Cheng, Ali Hatamizadeh, Ximing Lu, Hyunwoo Kim and Amlan Kar for their helpful comments and feedback on an early discussions and draft of this paper.

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Reviews:
[{"review_id": "qib98vMyqp", "reviewer": "Reviewer_EHkH", "summary": "This paper introduce LongPerceptualThoughts, a syhthetic visual reasoning dataset consisting of 30K long reasoning traces on visual perception tasks. \n\nLongPerceptualThoughts is built based on a carefully designed 3-stage pipeline: QA generation based on dense caption with LLMs, VQA reasoning generation with vision language models and longer reasoning traces generation with text reasoning models.\n\nExperiment results show that LongPerceptualThoughts can enhance off the shelf vision language models' perforance on vision-centric tasks by 3.4 percent. And also surprisingly improves their performance on text reasoning benchmarks (MMLU-pro) by 2 points.", "questions": "", "limitations": "", "rating": 7, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "The reasearch question is very important. Although straightforward, fine-grained visual perception is a field that current MLLMs still fall short on without a clear solution. This paper explores the usage of long-thinking to solve this problem and see some benefits, which is valuable to the research community.\n\nThe writing is very clear and easy to follow, the experiments are thorough and compelete, demonstrating the effectiveness of the proposed dataset.", "reasons_to_reject": "Althought the overall experiemnt is clear, some details are missing. The one that I curious the most is the number of input visual tokens in Table1, especially on V* benchmark, a dataset containing high-resolution images and questions about visual details. I believe the 48.51 perforamnce on V* is obtained by controlling the number of visual tokens to a certain threshold (i.e. the high-res image has to be down sampled to fit the number of visual tokens since it used native resolution naViT). Will the LongPerceptualThoughts model still surpass the Qwen2.5-VL-7B-Instruct when we don't set a budget limit for visual tokens?\n\nLongCoT can also be harmful for MLLMs since they tend to hallucinate more when its output is longer[1]. It would be great to discuss about this aspect since the dataset is endowing such ability to MLLMs.\n\n[1] Huang, Q., Dong, X., Zhang, P., Wang, B., He, C., Wang, J., ... & Yu, N. (2024). Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13418-13427).", "questions_to_authors": "LongCoT can also be harmful for MLLMs since they tend to hallucinate more when its output is longer[1]. It would be great to discuss about this aspect since the dataset is endowing such ability to MLLMs.\n\nAnother relevant visual cropping/searching paper[2] you might want to discuss in related works, where they follow model's own inference time dynamics to help the model zoom into the area of interest.\n\n[1] Huang, Q., Dong, X., Zhang, P., Wang, B., He, C., Wang, J., ... & Yu, N. (2024). Opera: Alleviating hallucination in multi-modal large language models via over-trust penalty and retrospection-allocation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13418-13427).\n\n[2] Zhang, J., Khayatkhoei, M., Chhikara, P., & Ilievski, F. (2025). MLLMs know where to look: Training-free perception of small visual details with multimodal LLMs. arXiv preprint arXiv:2502.17422.", "extra_scores": {}}, {"review_id": "b5tMhzQoYL", "reviewer": "Reviewer_DSu2", "summary": "The paper presents LongPerceptualThoughts, a synthetic dataset of 30,000 long-form CoT traces designed to equip vision-language models (VLMs) with System-2 reasoning skills like verification, subgoal setting, and backtracking. Using a novel three-stage pipeline—generating verifiable MCQs from image captions, distilling simple CoTs, and expanding them with a frontier reasoning model—the authors fine-tune Qwen2.5-VL-7B-Instruct. This results in notable performance gains across five vision-centric benchmarks and surprising improvements on a text-only benchmark (MMLU-Pro), suggesting strong generalization.", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "* Compared to previous methods, the paper has successfully constructed a solid SFT dataset, where previous methods failed to correctly construct the reasoning SFT data for new domains. The performance improvement has shown improvement after SFT training.\n\n* The data generation pipeline can be helpful for solving other similar tasks.\n\n* The paper has conducted insightful analysis of where the performance improvement comes from and analyzed reasoning patterns such as subgoal setting, verification, and backtracking.", "reasons_to_reject": "* Missing details and prompts used for constructing the dataset. As prompt engineering is important here for successfully constructing the dataset, it is good to put the prompts into the paper appendix. And it also becomes more clear how \"Wait\", \"Hmm\" are added, and how it affects the generation.\n\n* The paper has only evaluated on multiple-choice questions. It would be better to evaluate other tasks other than perceptual. It would be interesting to see if the method can generalize to other reasoning tasks, or the method is only limited to the selected tasks in the paper.", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Y0jS9sDxfO", "reviewer": "Reviewer_WtLJ", "summary": "The paper introduces a three-stage data synthesis framework designed to enhance visual reasoning in vision-language models (VLMs) by generating long chains of thought (CoTs) data. The framework synthesizes verifiable multiple-choice questions from dense image descriptions, extracts simple CoTs from VLMs, and expands these CoTs into elaborate reasoning traces using advanced reasoning models. The authors demonstrate significant performance improvements on several vision-centric benchmarks, including a notable gain on the V* Bench.", "questions": "", "limitations": "", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "- Presents a data-centric method for enhancing visual reasoning by aiming to distill complex, system-2-like reasoning long CoTs into the synthesis dataset.\n- Demonstrates the three-stage synthesis framework's effectiveness through substantial performance improvements on multiple benchmarks. \n- The proposed dataset synthetic method could improve the model’s performance on challenging text reasoning benchmarks, offering a promising direction for future research.", "reasons_to_reject": "- Lacks comparisons to existing visual reasoning methods related to CoT, like those modifying inputs or using intermediate CoT representations, which makes it difficult to identify the novelty/advantages/overall positioning of the proposed method within the existing literature.\n- The ablation study presented is limited, failing to clearly delineate the individual contributions of each component within the proposed framework. \n- The key process of thought-expansion is not well justified. When the generated CoTs distributions remain within the simple CoTs distribution from VLM, how could it diversify the cognitive behaviors?", "questions_to_authors": "- How does the performance of LongPerceptualThoughts compare with methods that modify input images or incorporate intermediate representations into CoTs? A detailed comparison would help to better understand the unique contributions of the LongPerceptualThoughts.\n- Can the authors provide a more comprehensive ablation study to validate the effectiveness of each component of the three-stage synthesis framework? This would include varying the number of multiple-choice options in Stage 1 and testing different trigger words in Stage 3.\n- What measures are taken to prevent overfitting to existing CoTs during the thought expansion process? How can the framework ensure that it explores truly diverse thought patterns rather than reinforcing existing ones?\n- How does the framework ensure that the generated CoTs remain within the model's original output distribution? Could the authors provide more details on the validation process for this aspect?", "extra_scores": {}}, {"review_id": "EKbvqWJm5x", "reviewer": "Reviewer_V9Qt", "summary": "This paper proposes to improve VLMs on difficult vision tasks through long CoTs. To do so, they synthesize a new dataset of long reasoning traces which then they use to fine-tune a Qwen model.", "questions": "", "limitations": "", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "Strengths\n--\n\n(S1) Scalable automated pipeline to generate the long CoT. First using an LLM to turn captions into MC questions, then frontier reasoning models to generate long-form reasoning traces.\n\n(S2) Improvements on a variety of tasks.", "reasons_to_reject": "Weaknesses\n--\n\n(W1) The title doesn't make sense given my understanding of the paper (maybe I misunderstand the whole paper, in which case the text has to be improved, not the title).\n\n- \"*System-2 Reasoning for System-1 Perception*\". What the authors do is to use a system-2 approach to solve vision-centric (perception) tasks that are usually addressed with a system-1 approach.\n\n- \"*Distiling*\": I don't see where there is a distillation happening here. The authors use the term in the text, e.g. \"Distill Simple CoT from VLM\", but I don't understand this usage of the term. I am familiar with the meaning of distillation in machine learning where a teacher model's output are used as supervision to train a student model. It seems here that a better word would simply \"*synthesize*\"? (instead of \"*distill*\")\n\n(W2) \"*verifiable multiple-choice questions*\": whatis verifiable? This is mentioned multiple times but it's never made clear what this means precisely, nor how this is used or enforced.\n\n(W3) There is little insight on exactly how/why the improvements are obtained. Which key aspects of the proposed data/generation pipeline are important? There is pretty much no support to demonstrate that specific aspects of the whole section 2 (and which aspects in particular) are important.\n\n(W4) Section 3.4 looks at response length. It notes that they are only slightly longer. I thought that the point of this paper was to enable longer reasoning? Shouldn't we expect the responses to always be **much** longer?\n\n---\n\nPlease proofread the paper as there are quite a few typos. Here are a few ones:\n\nL333 \"*reqrequires*\"\n\nL340 \"*alsothe*\"\n\nL341 \"*by +2 points by +2 points*\"\n\nL236/240: repeated paragraph header? (almost the same)\n\nL281 *performances* -> *performance* (singular)\n\n---\n\nMain reason to reject: no scientific analysis or take-aways. The authors describe a method, evaluate it and show some improvements. No ablations etc. Not sufficient for publication in its current form, but we encourage the authors to extend this work and this could become a very interesting paper.", "questions_to_authors": "See W1-4 above.\n\n(I am very unlikely to improve my score because of the \"main reason to reject\" stated above.)", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper proposes LongPerceptualThoughts, a synthetic dataset of 30,000 long-form CoT traces designed to equip VLMs with System-2 reasoning skills like verification, subgoal setting, and backtracking. A three-stage automatic pipeline is introduced, which consists of: 1) multiple-choice questions (MCQ) generation, 2) simple CoT extraction from a base VLM, 3) CoT expansion with a stronger reasoning LLM. A Qwen2.5-VL-7B model is fine-tuned on this data (supervised fine-tuning (SFT), and then direct preference optimization (DPO)), which yields gains on five vision-centric benchmarks and MMLU-Pro (text-only benchmark).

This paper received scores of 7, 6, 4, 4 before the rebuttal. After the authors’ response, all reviewers maintained their original scores. There was also some discussion between reviewers and authors, and the AC gave additional time for reviewers to further discuss.

Among strengths, reviewers acknowledge that "this paper explores the usage of long-thinking to solve this problem and see some benefits", that this work has "successfully constructed a solid SFT dataset, where previous methods failed to correctly construct the reasoning SFT data for new domains", and consider the "analysis of where the performance improvement comes from (including reasoning patterns such as subgoal setting, verification, and backtracking)" to be insightful. Additional positive results on CV Bench, MMVP and DocVQA were also provided during rebuttal.

Some concerns include:
- Lacks comparisons to existing visual reasoning methods related to CoT: the method is only compared to one baseline (Visual-CoT, provided during rebuttal) on a single benchmark, which limits the strength of the evidence; this was pointed out by reviewer WtLj, but authors did not further replied with clarifications.
- The lack of ablation studies and output analysis makes it difficult to identify what drives the improvements. Without these, the contribution is unclear and offers limited value to the research community; this is a concern raised by WtLj and V9Qt.
- Most benchmarks remain multiple-choice or contain short answers; reviewers question whether long-form reasoning is truly exploited; however, authors show DocVQA results during rebuttal.

After weighting both strengths and remaining concerns, this AC leans towards accepting this paper. Yet, it is important to understand where methods success or fail, and authors are encouraged to provide additional empirical evidence.
Decision: Accept