Game-RL: Synthesizing Multimodal Verifiable Game Data to Boost VLMs' General Reasoning
Authors: Jingqi Tong
Jixin Tang
Hangcheng Li
Yurong Mou
Ming Zhang
Jun Zhao
Yanbo Wen
Fan Song
Jiahao Zhan
Yuyang Lu
Chaoran Tao
Zhiyuan Guo
Jizhou Yu
Tianhao Cheng
Zhiheng Xi
Changhao Jiang
Zhangyue Yin
Yining Zheng
Weifeng Ge
Guanhua Chen
Tao Gui
Xipeng Qiu
Qi Zhang
Xuanjing Huang
Venue: ICLR 2026 Poster (2026)
Keywords: Vision Language Model
Reasoning
Data Synthesis
Game Playing
Visual Question Answering
Data Sets or Data Repositories
Benchmarks
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose Game-RL, constructing diverse game tasks for RL training to boost VLMs’ general reasoning ability.

Abstract: Vision-language reinforcement learning (RL) has primarily focused on narrow domains (e.g. geometry or chart reasoning). This leaves broader training scenarios and resources underexplored, limiting the exploration and learning of Vision Language Models (VLMs) through RL. We find video games inherently provide rich visual elements and mechanics that are easy to verify. To fully leverage the multimodal and verifiable rewards in video games, we propose Game-RL, constructing diverse game tasks for RL training to boost VLMs’ general reasoning ability. To obtain training data, we propose Code2Logic, a novel approach that adapts game code to synthesize reasoning data with unlimited examples and controllable difficulty gradation, thus obtaining the GameQA dataset of 30 games and 158 verifiable tasks. 
Remarkably, RL training solely on GameQA enables multiple VLMs to generalize across 7 diverse out-of-domain vision-language benchmarks, demonstrating the value of Game-RL for enhancing VLMs’ general reasoning. Furthermore, game data provides improvements comparable to general multimodal reasoning datasets (e.g. geometry/chart). More importantly, scaling up game diversity or game data volume consistently improves VLMs' generalizable reasoning capabilities. Our findings highlight scaling reinforcement learning in game environments as a promising direction for enhancing generalizable multimodal reasoning in foundation models. All code, dataset and model weights are released at \url{https://github.com/tongjingqi/Game-RL}.

Published as a conference paper at ICLR 2026
MULTIMODAL VERIFIABLE GAME DATA TO BOOST VLMs GENERAL REASONING
Jingqi Tong 1.2 Jixin Tang' Hangcheng Li Yurong Mou'r, Ming Zhang', Jun Zhao't L ,Chaoran Tao ZZhiyuan Guo ,iizhou Yu! Tianhao Cheng' Zhiheng Xi', .fanngha Jiang' .Zhangyue Yin' Yining Zheng'. Weifeng Ge
Yanbo Wen' Fan Song' ,Jiahao Zhan', Yuyang Lu
Guanhua Chen Tao Gui Xipeng Qiu1.2.31 Qi Zhang Xuanjing Huang
'Fudan University "Shanghai Innovation Institute 8 "Shanghai Key Laboratory of Multimodal Embodied AI
"Shanghai Artificial Intelligence Laboratory "Southern University of Science and Technology iigtog255 2mm. fudan. edu. cn, (zhaoj19, xpqiu, qz ) fudan. edu. en
ABSTRACT
Vision- Lasgua-aaguuag reinforcement learning (RL) has primarily focused on narrow do- mains (c.g. geometry or chart reasoning). This leaves broader training scenar- ios and resources underexplored, limiting the exploration and learning of Vision Language Models (VLMs) through RL. We find video games inherently provide rich visual elements and mechanics that are easy to verify. To fully leverage the multimodal and verifiable rewards in video games, we propose Game-RL, con- structing diverse game tasks for RL training to boost VLMs general reasoning ability. To obtain training data, propose Code2Logic, novel approach that adapts game code to synthesize reasoning data with unlimited examples and con- trollable difficulty gradation, thus obtaining the GameQA dataset of 30 games and 158 verifiable tasks. Remarkably, RL training solely on GameQA enables mul- tiple VLMs generalize across diverse out-of-domain vision-language bench- marks, demonstrating the value of Game-RL for enhancing VLMs general rea- soning. Furthermore, game data provides improvements comparable to general multimodal reasoning datasets (e.g. geometry/chart), More importantly, scaling up game diversity game data volume consistently improves VLMs' generaliz- able reasoning capabilities. Our findings highlight scaling reinforcement learning in game environments as promising direction for enhancing generalizable multi- modal reasoning in foundation models. All code, dataset and model weights are released https: http::///qtthb... com/tong http:////ggtttt..oooooooooogoooo
RL.
INTRODUCTION
Vision-Language Models (VLMs) have achieved impressive progress in basic tasks such image description and vision question answering. However, they still struggle with diverse and complex tasks that require multi-step reasoning in real-world scenarios (Lu all 2023: Zhang all 20248). One key reason is that vision-languag, RL primarily focused on narrow domains, mainly including geometry (Png et al,, 2024: Zhang et all, 2024c) and chart reasoning (He (2024), shown in Table M. This leaves broader training scenarios underexplored, limiting the exploration and learning of VLMs through RL (Lu 2023) Lm et all 2025: Jiang et al,, 2024al Zhao el all 2024 Wang aLa 2025).
We recognize that video games have three inherent advantages: First, they have rich visual elements and scenes with texts. Second, their mechanics are easy verify, with verifiable results. Third, their environments are fully controllable and easy to modify, providing convenience for difficulty control. Although existing works (Zhang all 2024:; Paglien et all, 2022)
synthesize reliable tasks
Zhang et all, 2025: Li et all 2022 found game a good arena to evaluate VLMs' reasoning ability,
"quual Contribution
Corresponning Authors

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Published as a conference paper at ICLR 2026
Table I: Our GameQA dataset extends RL training scenarios for VLMs to the domain of video games, providing diverse verifiable game tasks along with controllable difficulty. Comparison of existing visual reasoning datasets. Size means sum of the number of VQA pairs in train set and test set. Ration. Annot. means has annotated reasoning process. 3D Scene means some games of it are 3D scene. Detailed introduction can be found in Section
Reasoning Related Work Domain
Train Game Ration. 3D
Adjustable Difficulty
Set
Count
Annot.
Scene
MAVIS (Zhag et al. (20124G)
N/A
MultiMath (Peng et all (20022)
N/A
xx
x
Math
Geol 70k (Gao
(2(023)
N/A
x
Math V360K (liang et all (2(24h))
N/A
x
ING- VP (Zhagg et al. (211225)
xxx
BALROG (Paghen et all (2(224)
X
6
X
VideoGaaeBBnnch (Zhang all (2(225
23
X
Game
VCbench aL (2024))
10
X
X
GameQA (Ours)
30
they did not apply training it. One possible reason is they did not transform game data into visual question and answer (VQA) format to adapt to training, as shown in Table II
To fully the multimodal and verifiable rewards in game, propose Game-RL, construct- ing game scenarios for RI. training to boost VLMs general reasoning ability. Then we propose Code2Logiic, novel approach that adapts game code synthesize game reasoning task data, as shown in Figure M Code2Logic establishes mapping from game code to reasoning logic. Using Code2Logic, we have constructed the GameQA, which is a visual question-answer (VQA) dataset to train and evaluate the reasoning capabilities of VLMs. GameQA includes 30 games, 158 tasks and 140K questions with controllable difficulty gradation, covering 4 cognitive categories, as shown in Figures 2 and E.
We then apply Group Relative Policy Optimization (GRPO) (Guo et all 2025) to train multiple VLMs exclusively on GameQA. Remarkably, despite training solely game tasks, these models demonstrate out-of-domain generalization, achieving consistent performance improvements across 7 diverse vision-language reasoning benchmarks (e.g., Qwwn2.55VL-7B improves by 2.65%). Fur- thermore, we find that training on GameQA yields improvements comparable to training on general multimodal reasoning datasets such MAVIS (Zhang et all, 2IZR) and MultiMath (Peng all 2024). More importantly, scaling up either game diversity or data volume consistently enhances VL.Ms generalizable reasoning capabilities.
Our main contributions are as follows:
We propose Game-RL, constructing game tasks for RL training to boost VLMs' general reasoning ability. To obtain training data, we propose Code2Logic, a novel approach that adapts game code synthesize diverse verifiable game task data with unlimited samples and controllable difficulty.
Using Code2Logic, we develop the GameQA dataset, which includes 30 games, 158 tasks and 140K questions with controllable difficulty gradation.
We show that VLMs trained solely GameQA through RL achieve out-of- generalization across 7 diverse vision reasoning benchmarks, demonstrating the value of Game-RL for enhanc- ing VLMSs general reasoning. Our findings highlight scaling reinforcement learning in game environments as promising direction for enhancing models' generalizable reasoning.
CODE2LOGIC: SYNTHESIZING VERIFIABLE GAME TASK DATA
We propose Code2Logic to synthesize verifiable game reasoning tasks data via adapting game code. Code2loggic comprises three core steps: game code construction, task template design, and data
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Published as a conference paper at ICLR 2026
Step Game Code Construction
LLM
Task Templates
Prompt: Create an interactive
Final Result: Data Samples G Generated by Data Engine
Sokoban game.
class Solisonn:
Question:
def init (xelf, 4, -..28
is Solobbnn puzzle plaper moves: Left,
self-playee - (4, 9)
self-boxes
Up. Down, Down, where will player end up?
self-goals
Options: [1]:1. [2] (1. 2] [3] (1. [4] (2, 1) [5]
Nalls
(2, 26]2166] (2, 3) [7] [3, [1) (3, 2)
Rove Goas, darocctoo)):
Analysts: Play ez posttion: (2.3)
9a ney_y
(n new () selfasalls:
ct...ttnoveee........ nove sels.bosss: bovess
Move 1- Lett Player moves
Irom (2, 3) to 12, 2).
naturn self. box(...) return self, sove playan(...)
Game Code
Data Engine
Irom (2, 2) to (1, 2).
Move Up: Player mores
Step 2: Came Task and QA Template Design
Final posttion: (2, 2). Choose option 5.
Prompt: Refine man- made Task Template.
Sokoban code:
Question Template:
Step 3: Data Engine Construction
This is a Sokoban pozzle... ... If player moves: <move seqp
Prompt: Write a program that executes Sokoban code to fill in Task template, and output the dataset. Sokoban code: Task template:
where will player and up?
Options: <option 1>... coption 8>
Answer Template:
class bataEngine:
Player position. <pas>>
gononoite_ wtteete__sooooooooeeeeeeoo comolo(self)::
Move 1- <dir> Player moves from sposs spos>
Move 2-
<thr>> Player moves from spoc> spos>.
state records [sokoban Eove for move noves]
neturn QA ol_treplatee format (state reccoods)
Final position: quas> Choose option enumber>.
Figure I: Overview of Code2Logic approach. The process involves three main steps: (1) Using LLMs to construct game code. (2) LLM-assisted design of the task templates including question and analysis templates based on the generated game code. Each task template condenses one type of reasoning pattern in the game. (3) Using LLMs to construct data engine that directly reuses the core game code from the first step, including functions like move. (4) After these main steps, the data engine is executed to fill in the task templates developed in Step 2 and generate data samples, as illustrated in the "Final Result" section.
engine construction for dataset generation, as shown in Figure M. Code2Logic establishes a mapping from game code to reasoning logie.
2.1 GAME CODE CONSTRUCTION
The first step is to construct the code of the target video game. Game code defines the state space and contains some core functions encoding the transition rules of the game. These functions can be reused and adapted for data engine construction.
Taking Sokoban as an example, it is simple enough to be easily built with one-line prompt using LLMs such Claude 3.5 and GPT-4o. Sokoban game state is only composed of wall, player, box and target, while action only includes moving in four directions. The prompt used for game code generation and pseudocode of the game is illustrated in Figure MI (Step 1). Mcanwhile, the move function in Sokoban's game code can inspire the "State Prediction" questions, such as "Predict where will player end up after these steps", and this function can be reused for data engine construction.
2.2 GAME TASK AND QA TEMPLATE DESIGN
The second step is to design game task and question- answer templates (QA Templates) according to each video game, Game task designing base on the visual element and action space of video game generated in the first step. Taking Sokoban as an example, we can design task about predicting the position of player after moves according to a game state screen, so we can propose a question and an answer. The question is "If the player moves left, up, down, down, where is the player?" and the answer is "The position of player is (2.2)." " Then we can generalize these questions and answers into QA Template, as shown in Figure II (Step 2). Meanwhile, can fill contents into QA Template to get many instances. Detailed QA Templates of Sokoban is provided in Appendix G.I.
In GameQA dataset, each task is defined same QA template. We categorize the tasks into three types of "Target Perception Task", "State Prediction Task" and "Strategy Optimization Task", with descriptions detailed in Appendix E.Il
group of samples from the same game that share the
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3D Reconstruction
Tangram
Mosd Becandtrattin Sen
[Baey] d2 Hooe position dernjeadenn:
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-Qvesilore Which of the postiers contaiis scecl?
Queetton mary pleces are main boand?
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sAmwer Option 12,33)
Piece (aranga)) around postion Bs -d
(Hardjaa/ltt vosel oddttion requirement:
pieces
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Introduction: Add cubes (voseis] uitel the
structuress front, side, and top views
6. Veriliing Oytinallty...
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main board complete the shape, Fieces must touch at least one edge. not just a comner Some pieces are shown below the board. Grids with same color belong
Introduction:
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<Arswer Opsion i. rotate D ses
Category: Spottal Perception Understinding
Sudoku
Sokoban
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moves needed to solve the pussle? Options:
postton 12.5P Oynons A red, green, Dimagrets <knalysis> Geductive reasoning process:
2, [B] [5] 9, (4] [5] (5)
chralysko Player poshion (1,3] proitton
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Game Category: Multi stop Reasoning
Game Category: Stranogic Planning
Figure 2: Four game examples from GameQA: 3D Reconstruction, Tangram, Sudoku, and Sokoban, each representing distinct cognitive categories. Each game displays two VQA examples consisting of: (a) current game state visualization, (b) targeted question, and (c) step-by-step reasoning with the answer. GameQA transforms complex game-playing tasks into this structured VQA format. See Appendix for more VQA examples of some representative games.
For task, we use LLMs to assist the design and refinement of the QA template, an example prompt for such refinement is illustrated in Figure M (Step 2). Additionally, LLMs can also design tasks, with example prompt shown in Appendix G72 In summary, designing a QA template is equivalent to extracting type of reasoning pattern from the video game code obtained in the first step. The third step then further instantiates these reasoning task templates into a dataset.
2.3 DATA ENGINE CONSTRUCTION FOR DATA SAMPLES GENERATION
The third step is data engine construction for samples generation. The data engine a program that, when executed, automatically generates task instances in batch according to task templates. LLMS are used to assist in constructing the data engine's code based on the game code obtained the first step. The prompt for guiding L.MMs to construct the data engine code is shown in Figure M (Step 3). The pseudocode for the data engine shown in Figure (Step 3). Taking Sokoban's 's state prediction task as example, the data engine program is constructed from four core modules:
Game environment initialization: This module randomly generates Sokoban environment by directly reusing the initialization functions from the Step I's game code. Sokoban environment includes the position of player, boxes, goals and walls.
This module proposes state prediction task instance by generating a multi-step random movement sequence.
Proposing task instance:
This module generates solution using a corresponding algorithm. For state prediction task, the algorithm simulates each action by reusing the movement logic from game code of Step 1, which handles all movement situations including collisions and box pushing.
Solving task instance:
QA data construction: This module fills the task templates. The movement sequence will be filled into the question template to become a question instance. The state transition trajectory will be filled into the answer template to become answer instance.
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Published as a conference paper at ICLR 2026
In Domain
Nacmitt
Out of Domain
3D Spatial Perception and Understanding
Pattern Recognition and Matching
Multi-step Reasoning
Strategic Planning
Figure 3: Overview of the GameQA dataset. The 30 games in GameQA are classified into four categories based on the core abilities required to solve game tasks. Appendix E provides definitions of these four game categories. The games chose as out-of-domain are not used for training; instead, they are used test the generalization performance of the model trained on the in-domain games.
2.4 DATA QUALITY ASSESSMENT AND DATA AUGMENTATION
We implemented manual quality verifications at each step of our method.
Game code verification: We verify the correctness of the game code generated by the models by manually running the game program. If errors are found, including vision issues in the game display or code logic errors, they are fed back to the LLM for regeneration. For complex game features that LLMs cannot generate correctly, we retrieved relevant open- source code and provided it to the LLM. Games created with external code are shown in Appendix E
During the data engine development process. LLMs generate an initial version based on the prompt. This version then manually tested. If the generated reasoning questions and answers do not conform to the templates from the second step contain errors, the LLM will be instructed to regenerate the data engine.
Data engine validation:
Data augmentation and filtering: Once the data engine is constructed, data samples can be easily generated batch, yet the answer processes often exhibit repetitive patterns. We therefore em- ployed LLM paraphrasing to perform data augmentation, detailed in Appendix PE Additionally, we applied data filtering to ensure the augmented samples were correct, of appropriate length, and without excessive textual repetition. Details of data quality assurance process is shown in Ap- Time spent on these main steps for each game in GameQA is detailed Appendix EX!
pendix E4!
3 THE GAMEQA DATASET
The GameQA dataset visual-language question swering dataset that transforms game-playing tasks into Visual Question Answering (VQA) format, as shown in Figure 2 GameQA includes 30 games, 158 distinct tasks and approximately 140K questions in total.
Table 2: GameQA train and test set statis- tics summary. Full table Appendix B
Statistic Category Train Set
Test Set
Total Games
20
30
The dataset has 30 different games classified into four categories based on the core abilities required to solve game tasks: "3D Spatial Perception and Understanding". "Pattern Recognition and Matching", "Multi-step Reasoning". and "Strategic Planning", as detailed in Appendix D2 The criteria on choosing these 30 games are listed in Appendix [ТW] As illustrated in Figure EL the 30 games are divided into two sets: 20 In- nn-oomain games for model training and 10 Out-of- -fooo-n games for testing generalization performance. The Out-of- oft-oo-Domain set was held
Dataset composition:
In- n--oominn
20
20
Out-of-Domain
0
10
Total Tasks
102
158
Total Questions
126,760
15,047
Multiple Choice
86.520
10,518
Avg. Choices
7.10
7.05
Fill-in-the- Blank
40,240
4,529
Unique Images
74,620
8,620
during training.
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All tasks verifiable: All questions in the dataset are either multiple-choice fill-in-the- blank. The multiple-choice questions typically have 7-8 options, while the fll-in-thhe-blane questions re- quire simple answers such numbers coordinates, with detailed statistics shown in Table Q The specific verification method in our training and quantified verification correctness are in Section 4.2
Difficulty levels: Each task is assigned with one of three difficulty levels of questions ("QA Level"). And data samples are divided into 3 difficulty levels based on image complexity ("Plot Level"), namely game state or grid size, controlled by code parameters, providing three perception and rea- soning difficulty levels for the tasks. Examples demonstrating these difficulty levels are in Ap- pendix II The training and evaluation result about difficulty are shown Tables I and M
4 GAME-RL: RL ON GAME TASKS
4.1 RL ALGORITIIM
We use the Group Relative Policy Optimization (GRPO) (Shao et all 2024) as our algorithm. We use the standard formulation from DeepSeek, with the loss function shown below.
1
TORPO (0) Elq ((i), ~ Tom (0
G
10s
To(or,cl,, 01,tt)
A,,,, clip
To(or,.99 0n,ce))
e, +e) A.s -3D (
min
Toor Tonna ot,
Tooi 00,111 0c,et)
4.2
REWARD DESIGN
LLM as judge. Although the GameQA tasks are verifiable, the answer of question can have different expression forms (e.g., (2, vs. "x=2, y=3"), making traditional rule-based judge methods (e.g., text matching) suffer from insufficient accuracy. We therefore Qwen2.5-32B 1-nn2..-32B-Imm -Inntrucct-- AWQ (Qwen et all 2025)) (the AWQ-quantized version) as an evaluator model (Zheng et al,, (2233). The evaluator model solely determines if the final answer is semantically equivalent to the ground truth, with details and prompt shown in HI Manual check on 300 randomly sampled cases confirms 100% verification accuracy, validating the evaluator reliably reflects answer correctness.
Outcome reward design of RI.. The reward signal used for training is solely based on the cor- rectness of the model's final answer. A reward of 1 is assigned if the evaluator model determines that the final answer semantically matches the ground truth, and a reward of () is assigned otherwise.
EXPERIMENT
5.1 EXPERIMENT SETTINGS
The detailed description of data preparation, training processes, and evaluation method can be found in Appendix C.
Hyperparameters of GRPO training. We rollout 12 samples per questions. The model was trained for one epoch. The learning rate was 2c-7, with 5% warm-up. The clipping value was set 0.2 and the KL-divergence coefficient |3 was
0.04.
Benchmarks. We evaluated the models on a set of dision-languag reasoning benchmarks consist- ing of our GameQA test set and public general vision reasoning benchmarks.
GameQA benchmark. This test set includes around 500 question-answer pairs for each of 30 games, totaling 15,047 samples.
General benchmarks. We use the MMMU validation set (Yue et al (0249) for testing general multimodal understanding, and include MMMU-Pro (Yue (0244) which features 10-option multiple-choice questions. To assess mathematical reasoning in visual contexts, we use Math- Vista (Lu all all (024)) (testmini, 1,000 samples), Math Verse (ZZhang et all 2024M) (testmini, 3,940

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Published as a conference paper at ICLR 2026
Table 3: Evaluation results on general vision benchmarks, which shows game-only training enhances general reasoning capability of VLMs. We fine-tune three VLMs (Qwen2.5VVL VL-BB (Bai et all 2025), VL2.558 (Chen all 2024) and Intern VL3-8B (Chen 2025) on 5K GameQA samples using GRPO, resulting in the models Game- Www.-RL--Qen..-V------ 8B and Ganee-RL-nterrVLL.--- VL3-8B, respectively. Performance improvement compared to the vanilla
RL-Intem
model is denoted by (?). Best performance per section is in bold. Evaluation details: Appendix C.
Models
Avg. (f)
MathVista
MMBench
CharXiv
MMMU-Pro
Baseline
Random
14.84
17.90
12.74
26.37
24.67
0.00
10.03
12.19
Proprietary Multimodal Large Language Models
GPT-4o
56.9 5
63.8
50.2
86.0
69.1
47.1
30.4
51.9
Claude-3.5. Sonnet
S.nnee
78.5
Gemini-2.5-Pro
73.1
77.7
65.9
88.3
79.7
62.9
66.0
71.2
Open Source Multimodal Large Language Models
Qwen2.5.VL. VL-332B
60.70
77.40
60.41
88. 13
63.83
47.50
33,80
53.83
Ovis2-34B
57.91
71.50
53.71
88,73
60.91
49.10
35,93
45.48
IntemnVL2..538B
55.62
68.60
48.38
86.93
57.53
41.50
39.40
46.98
LLAVA-OV-72B
49.51
58.60
46.85
83.13
52.74
35.20
33.87
18
Qwen2.5.VL VL-
60.95
75.50
56.87
86.80
65.34
48.10
40.60
53.45
1ntrmVL2..-788
57.96
70,20
52.34
88.33
61.84
42.70
39.93
50.38
Qwen2.5 VL.7B VI--2.5-VIL-BB
50.00
66.62
45,10
84.05
49.78
37.92
30.17
36.32
Game RL Owen2.5-VL-76
52.65 (+2.65)
68.48
48.60
85,00
51.96
42,08
32.48
39.99
VMt.m\VL...5888
45.80
57
35.85
81.90
47.92
31.68
28.62
37.20
Game RI. Intem/1....5588
48.40 (+2.60)
62.38
38.72
82.18
48.91
35.66
31,87
39.08
VL3-mBLL-8B
54,15
68.72
49.76
98.98
56.85
38.92
35.24
43.59
Game RI--enterVT
56.05 (+1.90)
73.24
51.40
86.36
57.82
40.75
38.05
45.10
Table 4: Training GameQA leads to generalization competitive to using outstanding general multimodal reasoning datasets. Evaluation details: Appendix C4
Avg. (*)
Avg. (1)
& Under
Spatial
Pataan
Matching
Multi-stap
Reasoning Planning
Math Math
CharXiv
Math
VL-72
27.09
49.94
37.70
MAVIS-KK (GRPO)
(+0.52)
27.61
(+1.59)
39.00
38.42
Mullinndid--een-RR--- (GRPO)
(+1.34)
28.33
(+1,92)
38.65
-MuliMMah-BKK
(+1.29)
28.58
27.45
(+2.877
39.91
C.mmeAA-SK (GRPO)
(+2.78)
(+2.37)
39.74
"MuliMath- 8K
CameQA SK.
(GPPO)
(+3.54)
30.93
28.20
28.78
34.26
32.99
(+3.29)
$3.23
69.20
48.02
$4.73
$3,21
42,10
34.47
40.89
samples), and Math (Wang et all 2024a (open subset, 3,040 questions). For general vi-
sual understanding, adopt MMBench (Lm 2023) (validation set), and for chart-based
reasoning, we use CharXiv (Wang el al,, 2024h).
5.2
MAIN RESULTS
By analyzing the models' performance in-domain games, out-of-domain game tasks, and general vision-language benchmarks, we find that:
Game-only training enhances general reasoning capability of VLMss Training on the GameQA dataset significantly improves performance on the in-domain games and exhibits strong generaliza- tion the out-of-domain games (Table ( These improvements further lead performance gains on the interactive game environments from the ING-VP benchmark (Zhang et al. 202E7) (Table i Remarkably, all the three VLMs trained solely on game data show robust generalization broader general vision benchmarks, achieving consistent performance gains across all seven diverse gen- eral vision reasoning benchmarks (Table B). These results suggest that the models have successfully learned transferable visual understanding and reasoning abilities from the GameQA dataset.
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Avg.
CharXiv
Marheerse
MathVision
54
50
36
44
42
40
32
50
38
46
30
Score
MathVista
MMBench
MMMU-PP
MMMU
89
522
68
84.6
51
67
83.5
38
50
0k
1k-0k
6k- 15k
18k 30k
o1:
1k: 1k-5k
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16k- 20k
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0k-100
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Figure 4: Scaling effect of game data volume on general vision benchmarks. We train Qwen2.5-VL- 7B-Instruct on total of 20k samples. For every 1000 samples trained, the model is evaluated. We divide results into 3 stages and average the scores per stage to show the upward trend more clearly.
Training solely on GameQA enhances VLMs' general reasoning abilities, with robust generalization unseen games (Table 5), interactive game environments (Table E), and 7 diverse general vision reasoning benchmarks (Table ED. These results demonstrate the models have learned transferable visual understanding and reasoning abilities after game-data training.
Takeaway 1
GameQA enables out-of-domain improvements competitive with using general multimodal reasoning datasets. To better understand the value of GameQA, we compare it with outstand- ing general visual reasoning datasets, including MAVIS (Zang all 2024c), Multimodal-Open- RI (Imms lab, 2025) and MultiMath (Peng al, PI244.. MAVIS includes various geometry and function problems.
is geometry-centered dataset. MultiMath compre- hensive and diverse multimodal math dataset. Based on Qwen2.5- VL--BB, apply the same GRPO training on 5k GameQA samples, 8k MAVIS samples, 8k Multimodal-Open-RR samples, 8k Multi- Math samples, respectively, to conduct comparative training. The results (Table !D show that despite using fewer training data (5k vs. 8k) that are also out-of-domain for the general benchmarks, the GameQA Attriined model is competitive compared to the models trained on geometry or function data, where the general benchmarks would be considered in-domain. These results demonstrate that GameQA high-quality data source which enables strong oft-of-domain generalization, suggest- ing the high potential of games in enhancing general reasoning abilities in models.
Multimodal-Open-
Training GameQA leads to performance gains comparable to using general multimodal reasoning datasets such as geometry datasets (Table E). This highlights the effec- tiveness of GameQA high-quality data source, suggesting games may serve valuable scenarios and resources to boost general reasoning abilities.
Takeaway 2
GameQA enhances model performance in mixed training. We train Qwen2.5-VLL using GRPO on a mixture of 5k GameQA and 8k MultiMath samples, as shown in Table M. The results suggest that GameQA can bring extra benefits when mixed with other dataset for training.
5.3 SCALING EFFECT GAME DATA VOLUME GENERALIZATION
We train the Qwen2.5-VL. 5-VL-7B model GameQA subset of 20,000 samples from the 20 in-domain games using the GRPO method. As shown in Figure AL the model's performance score demon- strates an overall upward trend on general vision benchmarks the amount of training game data increases. This indicates that scaling up game data volume consistently enhances the VLM's generalizable reasoning abilities.
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No Chinge
Visual Perception Worsmed 5.71%
No Chazge
Dest Reasoning Worsoned
Original Perf
6.63%
XMMU
8, 5..5%
Text Recsoningg Improved
42.1
1039
.28.0
28.1
Visual Peroeption Imprrved
Question tact data sones maintuirs
MMMMU-PI
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30.8
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highest values reliivelly throughout time
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28.8
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Figure 5: The scaling effect of game diversity. As the VLM is trained on increasing num- ber of distinct games from GameQA, the perfor- mance general visual benchmarks improves.
Figure Impact of Game- RL on visual percep- tion and text reasoning abilities on general vi- sion benchmarks. Two pie charts show both abil- ities improve, with perception case below.
5.4 SCALING EFFECT OF GAME DIVERSITY ON GENERALIZATION
With around 5,000 total training samples, we trained the Qwen2.5-VL-30 on GameQA subsets with 4 and 20 distinct games (Table R. Performance on general benchmarks improves as game diversity increases (Figure 5D This suggests that scaling game diversity makes better generalization (Xi aal 2025), enabling the model to acquire more robust visual understanding and reasoning abilitics.
Takeaway 3 Scaling up either game diversity (Figure + or game data volume (Figure Ai con- sistently improves VLMs' generalizable reasoning capabilities. These results suggest scaling reinforcement learning in game environments offers promising pathway toward more general- izable multimodal reasoning in foundation models.
5.5 QUALITATIVE ANALYSIS
To confirm that GRPO GameQA substantially enhances visual perception and text reasoning abil- ities of models, we manually analyzed 790 cases randomly sampled from the results of Intern V1.2.5- 8B, containing responses before and after GRPO. The results (Figure B confirm that after GRPO, the model demonstrates improved recognition of visual information and performs more precise text reasoning. More statisties and cases illustrated Appendix B. In addition, our quali- tative analysis of model performances across four game categories reveals common behaviors and challenges, detailed in Appendix II
Takeaway 4 Beyond guantitative metrics, manual qualitative analysis (Figure 3 illustrates that Game-RL strengthens both visual perception and text reasoning capabilities of VLMs.
5.6 ADVANCED VLMS PERFORM NOTABLY WORSE THAN HUMANS ON GAMEQA
Both leading open-source and proprietary models achieve average accuracy levels considerably lower than those of human (Table 5) This clear difference highlights the difficulty and high re- quirements of the GameQA benchmark, requiring not only accurate visual comprehension of game scenes but also the ability to carry out multi-step logical reasoning. Furthermore, our qualitative analysis and case study in Appendix II reveal that even the most advanced models currently strug- gle to match human-level understanding, particularly in tasks demanding deep reasoning. More experiments are in Appendix [

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Published as a conference paper at ICLR 2026
Table 5: Evaluation results on GameQA benchmark. In-domain and out-of-domain game category results are shown. The percentage of performance improvements compared to the vanilla model is denoted by (t). Best performance per section indicated in bold.
Models
C?
3D Spatial Porc: Unier
Panee Recog. & Masching
Donsin Corree
step Krasceing
Straapii Porning
3D Spatial Poro & Under
Out Domain Gameo
Patienn Recog. Miaching
Milli step Reasering
Stratepic Planning
Beelins
Human
$4.75
88.18
ML.74 12:04
84,46
88.62
$1.61 9.68
29.17
73.81 9.50
K1.27
92.19
Random
11.90
11.09
10.24
13.61
7.11
11.53
10.27
Propeienay Multinodil Large Lasguage Models
Clauke3.S
37.41
$6.09
43.62
Cladde Soornctt
46.58
31:12
65.90
48.57
Gmmini2.54PP
38,98
46,03
$2.79
74.62
61.46
67.00
57.60
77.39
77.62
57.80
Multinedal
Medeli
Ova2-8B
34.05
10.02
24.43
27.21
20.37
26.99 99
20.70
20.70
54.14
23.41
BmmmVL334
26.09
31 19
22.51
12.55
30.05
26.73
25.30
30.34
53.13
19.15
LLaMA3.2- 1B-VVins
19.69
19.12
16.45
21.30
21.36
18.04
18.40
14.92
17.73
21.31
Queen 5-11-328
14
40.27
10.85
15.00
31.00
Ova2-34B
39:46
38.05
34.20
Intem\1.3.368
15.21
LLaVA UV. 728
24:30
19.92
24.55
23.53
Qwu.2.--VB-728
37.63
29.47
32.85
45.76
42.42
30.22
35,00
37.38
46.36
36.75
3memV1225776
12:35
27 15
25.54
79.53
33.90
15. 30
32.00
37.26
62.41
25.75
bicmVL3-758
38.00
33.15
33.03
46.60
39,20
39.74
34.90
40.70
$9.95
32.43
IntmmVL2...388
KL, 18
(+7.42)
26.74
29.51
35:44
2537
25.12
IiemVL3338 4B
26,51
22:53
50.18
31,45
27.64
27.44
23:91
Game Son--Bl-lmeeeNl..a.
33,9 (46.5)
27.94
28.52
36.81
39.07
28.00 (+1.1
20.30
25.31
34,99
36,09
Qwen2.5-VVL-7
25.78
22:58
21.92
25.21
33,40
27.00
23.00
29.30
26.21
29.34
Came-Rl--uerh S-V128
(2.12
33,34
41.45
36.51 (474
23.10
31.56
RELATED WORK
Multimodal reasoning data construction. Currently, the data construction methods are mainly divided into two categories: human expert supervision and automated synthesis. Peng et all (2(22)
and Lu et all (202I) collect visual reasoning problems from textbooks.
all (2023) constructs datasets through labeling by STEM students, but they are limited by the scarcity of high-quality data sources and the high cost of manual annotation. Lu ell (202I);; He all (2024); Kiao et (2023);
Sh et al (2122) uses expert models to generate reasoning processes, but the results are limited by the performance
the expert model. Trinh et all (2024)) and Zhang et all (2024c) synthesize geometric reasoning data through procedural methods, but these methods are often designed for specific domains and have high transfer costs. Table provides a comparison of existing vision language reasoning datasets.
Using game data to enhance VLMs' reasoning capabilities. Game environments provide well- defined rules and mechanies that are easy to verify. However, prior work has primarily used games either to train task-specific agents or to build evaluation benchmarks, without exploring how to use game data to improve VLMs' general reasoning abilities. For instance, RRed to train generalist agent through pure supervised training, but it is difficult to generalize on out- of-domain game tasks. Meanwhile, Paghen et al] (2022); Zhang et all (20244; 2025);: et all (2024) all establish gaming environments for VLMss but these environments are used exclusively for evaluation purposes rather than training. These limitations indicate that how to effectively use game data to enhance the reasoning ability of visual language models remains a critical problem to be addressed. Table provides a comparison of existing game reasoning benchmarks.
(2022) claims
CONCLUSION
To explore broader training scenarios and resources for vision-language RL, we propose Game-RL to construct game tasks for VLMs' RL training. We also propose the novel Code2Logic approach to synthesize diverse verifiable game reasoning tasks, obtaining the GameQA dataset. Multiple VLMs trained through RL solely GameQA achieve oft-of-domain generalization across diverse gen- eral vision reasoning benchmarks. Our findings highlight game environments as a new RL scaling dimension for enhancing Al's generalizable reasoning.
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STATEMENTS
Ethics Statement. This work adheres to the ICLR Code of Ethics. Our study does not involve hu- man subjects, sensitive personal information, or private data. All datasets used experiments were either synthesized by our proposed Code2Logic pipeline or publicly available benchmark datasets, and no additional ethical approvals were required. We have carefully considered issues of fairness, reproducibility, and research integrity. To minimize potential harm, we ensured that the synthe- sized data contains only game-based, rule-driven reasoning tasks without personal or discriminatory content.
Reproducibility Statement. We have taken multiple steps to ensure the reproducibility of our re- sults. Section B provides the construction details of the GameQA dataset, including task categories, difficulty levels, and statistics. Section 2 and Appendix EN describe the Code2Logic pipeline in detail, with pseudocode and examples of task templates. Experimental settings, model training de- tails, and evaluation metrics are reported in Appendix Q. addition, all prompts and augmentation procedures provided in Appendix [iL.
ACKNOWLEDGEMENTS
This work was partly supported by the National Natural Science Foundation of China under Grant Nos. 62521004 and 62376061, and by the Shanghai Science and Technology Committee under Grant No. 24511103908
The valuable efforts of the following individuals in the data synthesis and validation processes were of great importance to the development of this project: (Sorted by last name, then first name) Ruifeng Chen, Yingqian Huang, Yutong Ke, Hengxi Lin, Yuanhao Ni, Qingyun Shi, Haitian Wang, Xiaoyong Wang, Yufci You, Juntao Zhang, Weixin Zhang, Yang Zhang.
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APPENDIX CONTENTS
Limitations and future work
16
B More Analysis
16
Improvements on Interactive Environments After Game-
16
B.2 GameQA Evaluation Results by Difficulty
16
Error Type Analysis
16
Experiment Details
17
Human and Random Baselmes
17
Data Preparation
17
Training and Evaluation Setail
19
Inference and Evaluation
21
D More Experiments
21
SFT Experiments
D.2 Training Data Difficuity Experiment
22
D.3 Model Scale Experment
23
D.4 Data Scale Experiment
23
Approach Details
23
Task Category
23
Selection Criteria of 30 Games in Game
24
Time Spent on the Man Steps of Code2Logie for Each Game
................................
24
Games Generated Using External Open-Source Code
25
More Information about the Game(AA Dataset
25
The GameQA Dataset Statistics
25
Dehnition of Each Calegory
25
F3
Data Augmentation
26
Data Quality Assurance
26
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Details on Sokoban Task Synthesis
27
Sokoban QA Templates
27
5.2
Supplementary Prompl
27
Prompts
29
Data augmentation
29
2
Traming
................................
29
Inference
30
nvaiintion
30
Case study model performance GameQA
31
Details on the 30 Games and More Example Data Samples in the Game@A Dataset
36
Spatial Perception and Understanding
36
Pattern Recognition and Matching
43
Mului-stpp Reasoning
50
Strategic Planning
60
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A LIMITATIONS AND FUTURE WORK
Using reasoning processes to conduct Supervised Fine-Tuning (SFT) has not achieved satisfactory out-of-domain generalization. Therefore, future work could explore methods better leverage these reasoning processes to enhance the model's general capabilities, such as employing reinforcement learming based on process supervision. In addition, GameQA currently involves single-turn game question answering. Future work could focus on developing training and evaluation methods for multi-turn interactions in gaming scenarios.
B
MORE ANALYSIS
B. IMPROVEMENTS ON INTERACTIVE ENVIRONMENTS AFTER GAME-RL
We evaluate the models on the ING-VP benchmark (Zangg al, (IZER) to assess the improvements in interactive environments. ING-VP is an out-of-domain game benchmark which includes six video games. The input is image of the current game state, and the model outputs specific move. The game state then changes accordingly, achieving the finish or generating the next input image for the models. Under the original benchmark setup, the model scores 0 across the games both before and after training. We therefore choose an easier setup.
Reasoning improvements from RL training GameQA transfer to interactive environments, as shown from the results of Qwen2.5-VL.-7B Table H After Game-RL. training, average comple- tion rate improves from 12% 24%, with substantial gains in Maze (18% to 36%) and Hanoi (26% to 62%).. To understand these gains, we further analyze the single-step accuracy (i.e., the proportion of moves that lead closer to finish). For Maze, single-step accuracy increases from 49% 54%; for Hanoi, from 33% to 43%. These single-step improvements cumulatively leads to the substantial increases in the overall task completion rates.
Table Performance on ING-VP benchmark (task completion rate, %) before and after RL training on GameQA, demonstrating improvements on the sequential decision-making visual game tasks.
Model
Average
Maze
Sokoban
Queens
Hanoi
15 Puzzle
Sudoku
Qwen2.5-VLL-7B
12
18
13
26
12
Game-RR--Qeen255-111-777
24 (+12)
36
15
62
4
24
B.2 GAMEQA EVALUATION RESULTS BY DIFFICULTY
The fine- rre-grained difficulty gradation in the GameQA dataset enables more systematic evaluation of the models. As shown in Table Z when either QA Level or Plot Level increases, the models' accuracy scores generally show noticeable decrease.
B.3 ERROR TYPE ANALYSIS
We manually check the reasoning process of 650 GameQA cases randomly sampled from V12255V before and after GRPO to identify the error types. We categorize the errors as 3 types: Visual Perception Error, Text Reasoning Error and both. The results are presented in Fig- ure M Besides, in Figures @ and & we supplement Figure B with two examples of text reasoning improvement and another example of visual reasoning improvement after GRPO training.
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Table 7: Evaluation results on GameQA benchmark by diffiiuuly. Scores are broken down by question difficulty (QA Level) and image complexity (Plot Level) within in-domain and out-of- domain game sets. The percentage of performance improvements compared to the vanilla model is denoted by (1). Best performance per section is indicated in bold.
Models
Avp (7)
QA Lovel
In Domain Camees by Difficulty
Plut Leael
Aug. <1)
QA Level
Out Domenn Gomes by Difficatty
Plat
Eary
Fany
Medium
Easy
Medium
Eary
Medium
Pruprictary Mullinodal Lange Langunge Mudels
GPT
41.56
43.
40.63
47.69
38 10.00
55.63
44.45
32,08
54.89
42.98
33,71
Clande 3.5 Sonnet
48.90
26
45.07
36,20
55.40
45.90
43,80
50.94
62,80
46.15
43,80
60.58
49.
42.26
Chande-4-Sonnet
48.80
58. 36
50.14
42.25
56.45
48 99
42.81
56.00
66.71
55.91
02.80
54.31
50.37
Gemini-2.-PPo
60.62
67.43
62.36
$6.21
67.48
62.42
$7.72
67.68
74.47
67.56
61.10
74.23
66.43
61.90
Open Source Multinodal Lurge Language Models
54.92
11 1-3--11----1-- Viviin
Qwen2.5 32B
43.52
38.07
34.93
33.00
25
11aWA-ON-728
23.67
13.
Qwen2 5-V1-228
97.13
ImcmmV122.57788
30.58
14.57
32:97
27.20
bncmWVL33778
52.37
1.2 8B
22.31
27.12
21.95
18.35
28.65
22.56
18.36
21.29
24.52
17.78
16.67
-GameQA (SF7)
47.33
(+25
$6.89
45.36
39.79
53.73
45.50
26.10
27.84
21.95
28.39
30.43
27.37
20,07
(GRPO)
36.37
26.55
28.14
23,67
24.41
22.92
22.85
IneemVL33-88
26.85
33,82
26.37
19.79
28.91
28. 20
24.00
27.49
32.20
26.62
23.55
32.59
26.46
22.99
GameQA (SPT)
$1,08 (-24.23)
63,08
47.66
43.22
58.93
48.69
48.74
27.35 (40.14)
39.56
22.00
22.31
35.36
24.83
21.19
GameQA (GRPP)
33.53 (+6.68)
39.73
32.17
26.99
37.16
32.37
32,50
29.14
34.36
29.41
23,07
34.81
27.31
24.85
Qwa.255VVL-78
26.02
36.25
25.11
17:75
28.29
25 691
25.67
27.25
31.67
25.
24.03
33.75
25:38
22.12
GameQA (SPT)
49,23 (1)3
68.19
47.47
39.13
55.56
47.65
46.60
30.33 (43.05)
42,00
25.
23.55
36,86
29.29
24.29
GameQA (GRPP)
32.41 (+6.39)
42.51
30.25
23.96
35.05
32.40
31.79
36,77
(43.52)
36.66
25.9%
27.57
38.17
28:87
24.66
C EXPERIMENT DETAILS
C.I HUMAN AND RANDOM BASELINES
We included two baselines for comparison: human and random.
We select approximately 20 questions from each of the 30 games, resulting in 623 questions. These are grouped into 30 sets and assigned to STEM undergraduates familiar with the games. Each question is presented in a PowerPoint slide (Figure IIO) using python pythen-pptx* and responses are collected via an online questionnaire."
Human Baseline:
Random Baseline: This represents the lower performance bound, calculated as the expected score from random guessing on multiple-choice questions, with fill-in-the-he blankhh--lakkkk tasks contributing zero
C.2 DATA PREPARATION
C.2.1 SFT DATA PREPARATION
Game data. First, we use our data engine to generate 5K task instances from 20 in-domain games, obtaining 100K samples in total. Then we perform data augmentation, as shown in Appendix EE Finally, we filter these samples as detail Appendix Ddl A total of 40K samples are selected for SFT.
Other datasets. We prepare the "Geo-Multi" dataset for data quality comparison in SFT experiments. Geo-Multi consists of 40K samples, with 20K randomly sampled from Multi- Math300K (Peng all 2074) and another 20K from Geo170K (Gio et all 2023).
C.2.2 REINFORCEMENT LEARNING DATA PREPARATION
GameQA data. We sample 5K samples from 20 in-domain games. This sample size is smaller than that used for SFT to balance performance and computational cost, as the GRPO training process is relatively resource-intensive.
http:://hhooo.pp....cacccheee.ee hhtps://wwwwwjxx.cm/
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Text Reasoning Improved
Question What is the shortest sequence of moves from position
position 1y? Options: ... [3] Up
Left Left...
Answe: [3]
Response Before GRPO:
... ...3. Move Left.*; From (3, 1), moving left to (3, 0) 4. Move
Right** From (3, 0), moving right to (3, 1) ... The answer (2).
ResponseAfter GRPO:
** Move Up**: From (3, 3) to (2, 3) ... get closer the target row.
**Move Left**; From (2, 3) to (2, 2) ... brings player closer the target column.
*Move Left**; From (2, 2) to (2, 1) ... places the player at the target position (2, 1) ... The
answeris [3].
Change
Change
Text Reasoning W Worsened
14.95%
6.43%
85.00%
79.19%
5.86%
8.57%
Text Reasoning Worsened
Game QA
General Visual Benchmarks
Text Reasoning Improved
Question Which point indicates birth rate equals death rate?
Correct.Aswerr E
Response Before GRPO:
...4. 4. Point D**; The population size reaches peak,
indicating that the birth rate equal to the death rate,
db
resulting in stable population size.
Response GRPO:
... Logistic Growth (Point D) Birth rates are still higher than death rates,
growth is slowing down.
Stable Population (Points E)+;; The population size stabilizes. At this point, birth rates equal death rates, resulting in a stable population size.
Figure 7: Impact of Game-RL on text reasoning performance, with two improvement examples.
Table 8: Selection of games for the game diversity scaling experiment.
3D Spatial Perception and Understanding
Pattern Recognition
Multi-step Reasoning
Strategic
Game Set
and Matching
Planning
4 Games
3D Reconstruction
Tangram
Word Search
TicTacToo
Spider Solitaire, Freecell, Tetris, Zuma,
Tents,
Sokoban, Space Invaders, Maze,
20 Games
2D Turing Machine, Langton's 's Ant, Rhythm Game,
3D Maze, Rubik's Cube
(include above + 16 new)
Ultra TicTacTToe
Color Hue
Star Battle
Other datasets. Details on the general reasoning datasets used for comparative training (Ta- ble (522 are as follows. We sample 4K samples from the geometry and function split of MAVIS (Zhang all 2024s), respectively. For multimodal-open-rll -eeed---ppe--------erifeeedd (Imms laf, 2025). we use all the samples. For MultiMath (Peng et all 2024)), we sample 8K samples.
To investigate the impact of game diversity model generalization, construct subset of games in addition to the full 20 in-domain training games. Table g details the specific games in- cluded.
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No Change
Visual Perception Worsened
No Change
Text Reasoning Worsened
85.67%
3.39%
79.19%
5.86%
10.94%
14.95%
Text Reasoning Improved
Visual Perception Improved
Question What is the correct height relationship
between the three numbered points?
Answer < =2
Response Before GRPO:
Point 1 is lower than Point 2 because the player must climb the ladder from Point to reach Point 2 The
answeris <2 3.
Response After GRPO:
Cube 3 is lower than cube 1. Cube 1 is at the same
height as cube 2. Therefore, the correct height
relationship is: 3 <1 2.
Figure 8: Impact of Game-RL, on visual reasoning performance on GameQA.
C.3 TRAINING AND EVALUATION DETAILS
C.3.1
MODELS
We trained three VLMs, Qwen2.5-VL-7B (Baietal, (025),, Intern Chenetall 20224) and @C 2025) on our data.
C.3.2 TRAINING HYPERPARAMETERS
LoRA-based supervised fine-tuning hyperparameters. In this setup, Low-Rank Adaptation (LoRA) Hu et all (202I) applied all linear layers of the language model. We trained the model for one epoch. The rank was set to 16, with alpha set to 32. The learning rate was 5e-5, including a 3% warm-up period followed by a constant rate.
GRPO-based reinforcement learning. In this setup, we conducted full-parameter fine-tuning of the language model while freezing the visual encoder and projection layers. We rollout 12 samples per questions. We trained the model for one epoch. The learning rate was 2e-7, with 5% warm- up. The clipping value was set to 0.2 and the KL-divergence coefficient 3 0.04. More hyperparameters are listed in Table g
C.3.3 COMPUTE RESOURCES
LoRA-Based Supervised Fine-Tuning. For the models in the 7-8 billion parameter range, this LoRA-based SFT training was conducted on a single A800 GPU and the training duration lasted around 15 hours or less for 40k samples (1 epoch).
GRPO-Based Reinforcement Learning.
7-8 billion parameter model Training 5k samples (1 epoch) required approximately 22 hours, utilizing five A800 GPUs, including resources for the deployment of the evaluator model.
3 billion parameter model The training process lasted approximately 18 hours for 5k samples (1 epoch) four A800 GPUs, also including the evaluator model's deployment.
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Visual Perception Errr
Question How many bishops are on the board?
CorTectAnserr 1
Response
The Bishops are located on the squares e3 and f7. T herefore, the answer is hishops.
21.00%
37.01%
29.62%
31.51%
Correct
Conrect
10.14%
8.56%
31.85%
30.31%
Both Visual and Reasoning Error
Before GRPO
fttr GRPO
Text Reasoning Error
Minesweeper Board
Question How many cells have been revealed? CorrectAnswer 12
Respanse
We need to count the number of cells that have either numbers or are flagged. There are cells with numbers. There are 3 cells
flagged with "F". Therefore, the number of cells that have been revealed 4 =.
2
Figure 9: V1.2.5-88 error types before and after GRPO show that Game-RL. increases correct ratio and reduces visual and reasoning Two cases show the two types of error in detail. Solid and dash lines connected to two cases means both are before training.
ore van diill tomeer
MANGE
NANT
+
16:11
+2+1
Figure 10: Two example PowerPoint slides demonstrating how the student participants, during the human baseline evaluation, took the tests and might write or draw the slides.
GPU usage. Training a 7-8 billion parameter model with GRPO-based RL required approximately 22 hours, utilizing five A800 GPUs. This GPU allocation included resources for the deployment of the evaluator model. For a billion parameter model, the training process lasted approximately 18 hours on four A800 GPUs, with this count also inclusive of the evaluator model's deployment. For the 32 billion parameter model, the LoRA-based training took approximately 22 hours using eight H20 GPUs with 141GB memory each, while the reward model was deployed on four A A100 GPUs.
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Table 9: GRPO Hyperparameters. Some hyperparameters are different when training 32B model.
Hyperparameter
Value
Learning Rate (non-32B)
2e-7
Learning Rate (32B)
5e-7
Batch Size (non-32B)
3
Batch Size (32B)
16
KL-divergence Coefficient (3) (non-32B)
0.04
KL-divergence Coefficient (3) (32B)
0.001
Number of Generations
12
Temperature
1.0
Top-p
0.85
Top-k
50
Optimizer
A
Warm-up Ratio
0.05
Weight Decay
0.1
C.4 INFERENCE AND EVALUATION
Besides trained models, also evaluated proprietary large-scale models such as GPT-4o (20240806) (OpenA), 2024) and Claude 3.5 Sonnet (20241022) (Anthropic, 2024). and open-source models that represent the current state the art, including Intern and Qwen2.5-VL-72B.
The inference and evaluation configurations are unified across the original models and our trained models.
Inference: The inference temperature parameter is set to 0.2. The prompt template is shown in Appendix HK
Evaluation:
We evaluate the generated answers using the LLM-as-a-judge approach (Zheng et all 2023), with Qwen2.5-72B-AWW acting as the evaluator and the evaluation prompt shown in H
For the main RL experiments (Tables B and [S) and the SFT experiments (Appendix D.II) report the single checkpoint that achieves the highest validation accuracy. The validation set is 1% hold- out split from the training data.
For the results in Table EL both the three trained models (Game-RL-og Haane-RRL-Qee2.55VV---1 Game-RL- VL.2.nVLL225--8
and their corresponding baselines (Qwen2.5-VL Intern Intern VL22.5-BB, VL3-88)L3-8B) are evaluated by averaging over five inference runs with different seeds (13, 65, 117, 15, 67) ensure the improvements robust.
Ro-ne-RL-IntemVLL33
For comparative training (Table H] and scaling effect analyses of game diversity (Figure H) we evaluate the top three checkpoints (based validation accuracy) and report their average per- formance. Averaging across multiple top-performing checkpoints reduces training noise and yields more robust estimates.
D MORE EXPERIMENTS
D.1 SET EXPERIMENTS
As described Appendix XL we perform additional supervised fine-tuning (SFT) experiments on the three VLMs. The results of these experiments are presented in Tables [0] and [ and we find:
The GRPO demonstrates an advantage over SFT in out-of-domain generalization, though SFT yields strong in-domain gains. When evaluating model performance, the effects of SFT and GRPO training methods showed clear differences across domains. Specifically, SFT training with the GameQA dataset led to substantial in-domain improvements: the Intern and Qwen2.5- VL-7B models improved their average accuracy by 24.51% % and 22.59% respectively on test set covering 20 games, reaching final scores of 46. 46.73% and 48.37% (Table IIO). However, while SFT
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Table 10: Evaluation results on GameQA benchmark. In-domain and out-of-domain game category results are shown. Performance improvement compared to the vanilla model is denoted by (t). Best performance per section indicated in bold.
Model
Avg
3D Syrciad Perc. & Under
Pattern Rociy Mancting
In Donsin Gonace
Maltioaap Reasening
Sirategi: Platning
Aug. (1)
3D Sputial Perc. & Under
Out Domoin Garse
Pallecn Rociy. Marching
Mallisdup Reasering
Strllgii Planning
17:18
25.
25.07
20,05 25.87 (-3
20.30
22.45
15.83
18,07
GameUA (SFT)
46.73 (+34,51)
42.38
45.55
51.56
47.64
25.81
25,90
28.25
GameQA (CRRP))
29.46 (+7.21)
26.24
26.05
29.51
35.64
25.00
25,12
24.91
20.45
InteraVL33-8 kg
25.51
22.55
21.91
20.15
31.43
27, 7.54
29.60
27.64
29.62
23,91
-amme24 (SPT)
90.72
(+34,21)
46.91
45.82
55.91
$8.63
26 (-1
24.20
14,00
32.01
3308
-amee24 (GRPP))
$3.09 (45.50)
27.94
28.50
35.81
39.07
18,00 (-1
79.20
25.31
34.89
26,09
Qwent VL-
25.78
22.58
21.92
25.21
33.
27.09
25.60
29.20
26.21
29.34
-Game24 (SFT)
48,37
(+221.09)
41.83
58.66
49.96
27.71
26.80
21:16
31.79
37,33
-Game244 (CBRP9))
(2.12 (26.4446)))
26.83
33.34
30.31 (+3.47)
31.24
32.13
Table 11: Evaluation results general vision benchmarks. Performance improvement compared to the vanilla model denoted by (t). Best performance per section indicated in bold.
Avg- (t)
Models
Vast
VathVerse
MMBench
MMMU
CharXiv
MathVision
MMMU-Pro
IntemnVL..588
45.89
$7.50
36.04
81.93
47.96
31.70
28.87
37.25
GameQA (SFT)
45.06 (:0.83)
58.10
35.79
82.87
48.07
31.20
36.56
Geo-Multi (SFT)
43.84(-2.05)
53.60
36.90
82.80
43.17
30.40
27.80
32.22
GameQA (GRPO)
47.91 (+2.02)
61.70
37.11
83.87
50.06
32.00
31.93
38.69
V-3-8
69.10
10
GameQA (SFT)
49.66(-4.82)
63,20
43.30
84,53
53.4 $3.44
32.90
29.60
40.64
Geo-Multi (SFT)
48.52 (-1.42)
62.60
40.96
82.87
48.54
37.80
29.80
37.06
GameQA (GRPO)
55.88 (+1.40)
73.00
50.71
86.20
58.34
39.90
37,93
45.10
Qwen2.5-VL.
49.94
66.80
45.08
83,67
49.01
37.70
30.80
36.49
GameQA (SFT)
47.26 (-2.72)
63.00
37.31
83.07
47.49
38.60
25.73
35.62
Geo-Multi (SFT)
48.52 (-1.42)
62. 62.60)
40.96
82.87
48.54
37,80
29.80
37,06
GameQA Q (GRPO)
52.27 (+2.33)
68.20
47.97
83.53
50.53
42.70
33.07
39.89
excels improving in-domain performance, it can sometimes lead performance degradation on general tasks as seen in I. a phenomenon known as "catastrophic forgetting". Crucially, for training sets that belong the mathematics domain, such as Geo-Multi (see Appendix (Z)), models trained on them can still exhibit a decline in general capabilities. This may reflect the tendency of the SFT method to overfit the model to specific domain data et (0225).
In contrast, when training on GameQA using the GRPO, models not only successfully avoided performance degradation on general tasks but also generally achieved performance improvements across multiple general visual benchmarks (Table R). A typical example is the Qwen2.5-VL-7B model, which, after GRPO training, showed enhanced performance on challenging benchmarks such MathVista. Verse, and CharXiv, reaching level comparable to larger-scale models like Intern VL2.5-38..
Pure RL outperforms SFT-then-RI in out-of-domain generalization. To validate choice of directly applying RL, we compare our approach to a two-stage SFT-then-RL. pipeline on Qwen2.5- VL-7B. As shown in Table I21 while the two-stage pipeline yields strong performance in-domain tasks, it leads to a significant performance drop of -2.5% on general vision benchmarks. This sug- gests that full-parameter SFT narrow, specialized domain like GameQA cause catastrophic forgetting. In contrast, pure RI. better preserves and enhances out-of-domain generalization capabil- ities, achieving +2.33% improvement.
D.2 TRAINING DATA DIFFICULTY EXPERIMENT
To analyze how reasoning difficulty impacts generalization, we categorized tasks into Easy (55-85% baseline accuracy), Medium (30-55%), and Hard (5-30%). We trained Qwen2.5-VL-77 on 5k sam- ples from different difficulty combinations. Table IV shows that training on a diverse mix difficul- ties yields the best generalization. The model trained on Hedyn+Medium+Hrdd samples achieves the highest average score (52.74), outperforming models trained on simpler subsets. This confirms that the complex reasoning patterns in GameQA crucial for improving general reasoning.
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Table 12: Comparison of training pipelines on Qwen2.5-VL-7B, SFT is performed 20k samples, followed by GRPO on 5k. Performance on general benchmarks is shown as a relative change from the baseline.
In-domain Games
Out-of-domain Games
General Benchmarks (Avg. Change)
Training Stage
Baseline (Before SFT)
25.89
26.92
0.0%
SFT
56.21
30.74
-3.9%
SFT-then-RL
58.08
31.96
-2.5%
Pure RL (Our Method)
32.12
30.51
+2.33% %
Table 13: Impact of training data difficulty generalization. Results averaged over the top three checkpoints. The baseline score is 49.94.
Training Data (5k)
Average
Math Vista
MathVerse
MMBench
MMMU
CharXiv
Math
MMMU-Pro
Baseline
49.94
66.80
45.08
83.67
49.01
37.70
30,80
36.49
Easy
52,40
67.87
47.93
83.91
51.58
41.77
33.73
40.03
Medium
51.95
67.30
48.10
83.64
50.53
40.83
33.96
39,30
Hard
67.57
83.58
51.03
Facy-Mediium
52.45
68.60
48.07
83.42
51.30
41.40
34.86
39.49
52.74
68.33
83.93
52.36
40.73
34.31
D.3 MODEL SCALE EXPERIMENT
To verify if GameQA's benefits extend larger models, we conducted experiments Qwen2.5-VL. 32B using LoRA. As shown in Table 14 the 32B model demonstrates greater performance gains than the 7B model under identical LoRA settings across all domains. The average improvement on general benchmarks is +0.78 for the 32B model compared to +0.68 for the 7B model. This trend suggests that GameQA an effective resource for enhancing reasoning larger-scale VLMs, and we hypothesize that full-parameter fine-tuning would yield even more substantial gains.
Table 14: Performance gains from GRPO (LoRA, 5k GameQA data) on 7B vs. 32B models. Gains are shown parentheses.
General Vision Benchmarks
In-domain Games
Out-of-domain Games
Model
Training
Baseline
49.94
25.78
27.09
Qwen2.5-VL-7B
+GameQA (LoRA)
50.62 (+0.68)
30.90 (+5.12)
30.45 (+3.36)
Baseline
60.18 60.96 (+0.78)
35.91
35.76
Qwen2.5-VL--32B
+GameQA (LoRA)
43.24 (+7.33)
40.77 (+5.01)
D.4 DATA SCALE EXPERIMENT
We extended our data scaling experiment up to 20k samples. While individual checkpoints exhibit fluctuations common in RL, "binned averaging" analysis (Table IS) reveals clear upward trend. By averaging performance across training stages, we smooth out short-term noise and observe a stable improvement trajectory. The average performance on general benchmarks steadily increases from +2.31 in the early stage to +3.02 in the late stage, confirming that model performance continues improve when we scale up more data from GameQA.
E
APPROACH DETAILS
E. TASK CATEGORY
Target Perception Task focuses on visual perception and basic state awareness. State Predic- tion Task, building directly the perceptual capacities, needs predictions about state transitions.
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Table 15: Binned averaging analysis of data scaling effect on Qwen2.5-VL-7B. Performance gains over baseline are shown in parentheses.
Training Stage
Sample Range
General Vision Benchmarks
In-domain Games
Baseline Model
Ok
49.94
25.78
Early Stage Training
Ik-5k
52.25 (+2.31)
27. (+1.90)
Mid Stage Training
6k-15k
52.57 (+2.63)
34.97 (+9.19)
Late Stage Training
16k-20k
52.96 (+3.02)
38.69 (+12.91)
Strategy Optimization Task then needs both perceptual and predictive capacities find optimal solutions. This progressive structure helps organize reasoning skills from simple to complex.
The conceptual outline for each category, using Sokoban an example, is as follows:
Target Perception Task:
Queries static information within the game state. For instance, questions ask about the position or number of boxes, and the answers list the specific positions of each box by directly inspecting the current state.
State Prediction Task: Infers state changes following actions. For instance, questions predict the player's position after sequence of moves. Answers are derived by analyzing the initial state, simulating the execution of each step, recording the resulting state changes, and thus determining the final player position.
Strategy Optimization Task: Aims to find optimal solutions. For instance, questions ask for the shortest path to push specific box its designated target. The answer is derived by first analyzing the initial state to determine the optimal route for moving the box to its target, and then simulating the execution of this optimal action sequence.
E.2 SELECTION CRITERIA OF 30 GAMES IN GAMEQA
We select these games based on the following criteria.
Ability coverage
The games need cover diverse range of reasoning abilitics, including 3D Spatial Perception and Understanding, Pattern Recognition and Matching, Multi-step Reasoning, and Strategic Planning.
Code simplicity The code should be easy to construct, meaning they are simple enough to be programmed by an LLM, or are open-sourced.
Static game They should be static or can be transformed into static state, so that problems can solved from a static image.
E.3 TIME SPENT ON THE MAIN STEPS OF CODE2LOGIC FOR EACH GAME
Figure illustrates the estimated time spent on implementing the main steps of the Code2Logic approach across all the 30 games in the GameQA dataset. Time cost game ranges from minimum 4 hours to a maximum of hours, with an average of 7.5 hours per game. This is relatively cost-effective and appears highly acceptable, especially considering that once the data engine code is built, it can generate an unlimited number of data samples at scale.
Furthermore, we anticipate the required manual effort and time cost will progressively decrease due to two key factors:
Advancements code agent capabilities: We used models like GPT-4o and Claude 3.5 in our work. With the emergence of more powerful code agents (e.g., Claude Code) and general models (e.g., GPT 5.2, Claude 4.6), the synthesis difficulty and human efforts will progressively decrease.
Improved annotator experience and efficiency: In our project, the data annotators were STEM undergraduates with limited experience, each handling only two games average. By employ- ing more experienced annotators (e.g., STEM graduates) and increasing their familiarity through repeated tasks, time cost per game be significantly reduced.
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Thi The i the 301 Eog.ootaarttg (barn)
Average (7.5 h)
12
10
10
10
10
Langtos Ani
Pyranid Chass
TieTaeTor
Jonnz
Klondike
Lifegant
Poeman
3D Reconstaaiio
Franell
2D Taring Maekme
Stadeko
50 Maze
Mauewweper
Huo
Rbythan Game
Sokeban
Space Invades
Star Bantl
Worl Sanhh
Zuma
Maze
Minecraft
Rabiks Catee
Spuder Sobiatre
Shake
Libs TicTacTTe
Tasts
Chezs Ranget
Tangram
Tatris
Game Name in GameQA Dataset
Figure 11: Estimated time (in hours) spent on implementing the main steps of Code2Logic across different games in the GameQA dataset, with an overall average of 7.5 hours per game.
E.4 GAMES GENERATED USING EXTERNAL OPEN-SOURCE CODE
This section lists the games whose code is generated based open-source code, as referenced in Section 2.
Spider Solitaire (Open-source Bolitaire)
code
URL: StEDS://qitie httpp//ww..oommdd//sssssssiccc
Based original Python implementation of Spider Solitaire, our code reused its core rules and GUI. And we simplified the game to a single suit (Spades) to reduce complexity, enriched initial setups through LLM-implemented random deals, and adapted the original game rules into detailed instructions.
Klondike (Open-source code URL: Attp:: http:://gg...au..ooooooooo/////p
Based on the original open-source Klondike Solitaire project built on Pygame we adapted the code using LLM into automated dataset generation tool. It reused the game engine and introduced a method for generating diverse random initializations.
Space Invaders
(Open-source
code
URL:
REEDS:/ SEEDs://qitub.com
space 1ive-invae invaders
Based the original Space Invaders game built with Pygame, utilized its elements and visual assets to generate static game scenes. Using an LLM,, we converted the dynamic game into static scene snapshots for dataset generation.
F MORE INFORMATION ABOUT THE GAMEQA DATASET
F.1
THE GAMEQA DATASET STATISTICS
The full GameQA train and test set statistics table is shown Table [T
F.2 DEFINITION OF EACH CATEGORY
3D Spatial Perception and Reasoning. These games involve the ability perceive, plan, and reason in 3D space to complete tasks such as navigation and spatial transformation. For example, in 3D Reconstruction, the task is reconstruct the stacking arrangement of 3D cubes from side view. Solving this task requires 3D spatial reasoning to establish the relationship between the 2D view and the 3D view.
hhtps:/wwww.pyyame...nwwwwsw
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Table 16: GameQA train and test set statistics summary. All lengths are calculated by words.
Pattern Recognition and Matching. These games require capability on discern- ing and matching visual patterns related to object shapes, colors, combinations, and other regularities. For example, in Tangram, the task is to identify which piece can fill the empty space, Solving this task requires recognizing the shape of void and matching with given pieces.
Statistic Category
Train Set Test Set
Overall Counts
Total Games
Total Tasks
102
158
Total Questions
126,760
15,047
Image Statistics
Multi-step Reasoning. These games fea- ture multi-step reasoning and iteratively applying rules to reach the solution. For example, in Sudoku, the task to infer which color should fill the empty space ensure that no colors are repeated in the same row, column, or 3x3 grid.
Unique Images
74,620
8,620
Avg. Image Width (px)
511.00
504.10
Avg. Image Height (px)
475.73
468.98
Question Characteristics
Avg. Question Length
275.27
272.43
Avg. Analysis Length
106.85
144.89
After Augmentation
300.79
Strategic Planning. These games require planning the optimal solution in optimiza- tion problems. For example, in Sokoban, the task is plan the shortest path for pushing box from the starting point to the target location.
Multiple Choice Questions
86,520
10,518
Avg. Choices for MCQs
7.10
7.05
Fill-in-the-llan
40,240
4,529
F.3 DATA AUGMENTATION
To prevent model overfitting specific reasoning patterns, employed LLM-based reasoning paraphrase strategy using VL2.57VL...578BB. We only provide with textual information, namely the question, original answer process and final answer, rewrite the answer process. This approach enriches linguistic style and logical expression diversity while maintaining semantic consistency.
F.4 DATA QUALITY ASSURANCE
To ensure the high quality and reliability of our synthetic dataset, implemented quality assur- ance process, consisting of four stages:
1. Human Inspection: STEM students inspected initial samples to ensure logical correctness, clar- ity and completeness of questions, images and reasoning steps.
LLMs Check: Fed samples to GPT-4o and Claude-3.5 Sonnet to ensure model comprehensibil- ity, identifying necessary refinements in the samples.
3. Fost-nuugmntttion Verification: Manually verified reasoning accuracy in a random subset of augmented data.
4. Automated Data Filtering: Removed samples based on length, high repetition (>70% 4-gram overlap), or wrong answers, reducing the total size from about 150k to 126,760.
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G DETAILS ON SOKOBAN TASK SYNTHESIS
G.1 SOKOBAN QA TEMPLATES
Here provide templates used for generating Sokoban puzzle samples, including the three problem types: Target Perception, State Prediction, and Strategic Optimization.
Target perception QA template. The Target Perception template (Table 17) is used to generate
questions about identifying the current position of game elements.
Table 17: Target perception QA template
qqesstion: "This is a Sokoban puzzle where Cartoon people is player green X is target, brown box with X is bex to push, brown tiles are walls, and light brown areas coordinates (x, y) in this puzzle represent the matrix format. What is the current position of the <object> (row, column) ?| nOptions:\n copticn_I>\n(2) <option_2>\n(3] <option_3>\n[] < aption_4>\n[5) coptien_5>\n[6] loption_6>\n(7) [aption_>>An(1 option_8>")
movable spaces. The
"answer": <number>,
Manalysis"; "Player position: spos>\nBoxes positions: <pos>\n AnTarget positions: <pos> AnTh player currently at position <pos>. An nSo the answer is <answer>. The option number is <number>.
State prediction QA template. The State Prediction template (Table I8) is used to generate ques- tions about predicting the final position of the player after a sequence of moves.
Table 18: State prediction QA template
"question" "This is a Sokoban puzzle where Cartoon people is player green X is target, brown box with X is box to push, brown tiles are walls, and light brown areas are movable spaces. The coordinates (x, y) in this puzzle represent the matrix format. Is the player makes these moves: <mov_ where will player end up?An'noptiion:: [n[]] <option_[>\n(21 coption_2>An[33 aption_3>\n [4] <option_4>\n(5) coption_5>Anfe <option_6>An17 < option_7>\n[8] <option_8>", "answer": <number>,
"analysis": "Player position: <pos> <dir>: Player moves from <pos> to <pos> 2 <dir>: Player moves from <pos> to <pos>\n... AnFinal >",
Aolsnal positien: <pos>inThe opticn number is <number
Strategic optimization QA template. The Strategic Optimization template (Table [T]] is used to generate questions about finding the optimal sequence of moves between positions.
G.2 SUPPLEMENTARY PROMPT
As mentioned in Section k LLMs can not only refine the human-designed templates, but also
design new questions and QA templates with the example prompt provided below. will conduct careful manual review of the quality of tasks generated by the LLM and make selections, even if we have already prompted to generate diverse and meaningful QA pairs.
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Table 19: Strategic optimization QA template
"question": "This is a Sokoban puzzle where Cartoon people is player green X is target, brown box with X is box to push, brown tiles are walls, and light brown areas are movable spaces. The coordinates (x,
in this puzzle represent the matrix format. Treat the boxes as walls, what is the shortest sequence moves for human to move himself from position <pes> to position <pos >2AnknOptions: [n[1] <epticn_I>\n[2] copticn_2>\nf3 <option_3>ly 3> [4] <option_4>>y [5] <option_5>\n[6] <option_6>ln[] <option_7>\n 7> [8] <option_8>",
"answer": hanswer_rumbert
Manalysis" "Player position: <pos>\nHoxes positions: <pos>\nTargee positions: <pos> \nstart position: <pos> \nEnd position: <pos>\ nOptimal move sequence: mov_segpinMowe
<dir>: Player moves <dir>; Player moves from <pos> to
from <pos> to <pos>\nMeve spos>\n... AnFinal pesitien: <pos>\n\nše the answer is <answer>. The option number is <number>.",
Prompt for designing new questions and the corresponding QA templates
Generate Game QA Derivative Templates Based the provided basic QA template for the
Sokoban game, please design more question-ans, question-answering answering template variations. The reference file already includes three basic template categories:
State Prediction Predict the player's position after move.
2. Target Perception Identify the current positions of game elements.
3. Strategy Optimization Find the optimal movement path.
Please design 3-5 innovative derivative templates for each category, ensuring the new tem- plates:
Maintain consistency with the original JSON format.
Cover different reasoning difficulties and complexities.
Test different cognitive and reasoning abilities.
When designing, please follow this reasoning hierarchy:
Level 1: Target Perception QA Focus on basic visual recognition and state understanding (e.g., "Where the box?").
Level State Prediction QA Focus on state changes and transition reasoning (e.g., "After performing these moves, where will the player be?").
Level 3: Strategy Optimization QA Focus on finding the optimal solution (e.g., "What is the minimum number of moves to push the box to the target?").
For each new template, please provide:
Template name and type classification.
2. Complete JSON template structure (including necessary placeholders).
3. brief description explaining the specific abilities tested by the template.
4. Placeholder filling examples (how to generate specific question instances).
Please ensure your template designs can generate diverse and meaningful Q&A pairs based on the game state and maintain consistency with the original template structure.
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H PROMPTS
H.1 DATA AUGMENTATION
Below is the prompt used to perform the LLM-based reasoning paraphrase strategy detailed in Ap- pendix E with visual information not provided for the model.
Prompt for data augmentation
Question:
(query) (the question generated by the data engine)
## Ground Truth: [response) (the answer with reasoning process generated by the data en- gine)
Based on the above question and the provided ground truth, the current process of providing the answer overly mechanical and simplistic. Please provide detailed reasoning steps based the content of the question and the reasoning steps in the Ground Truth. The reasoning steps should be detailed, logical, and consistent with the Ground Truth.
Additionally, before starting the reasoning, emphasize: will carefully analyze the question and the image and provide detailed reasoning steps." Do not include statements such as "This matches the provided Ground Truth" " or similar expressions in your response.
Please follow the above requirements to provide a detailed analysis, reasoning, and answer.
H.2 TRAINING
System prompt for the policy model trained in GRPO
Please carefully observe the image, thoroughly understand the conditions provided in the question, use logical reasoning to arrive at the result, and reflect on and verify the reasoning process ensure the accuracy of the answer. Finally, provide the correct answer.
We extract the final answer of the policy model's response using rule-based parser (c.g.. by finding phrases like "The answer is...). The evaluator model 2.n25--32BBIInstucct solely deter- mines if this final answer is semantically equivalent to the ground truth, with prompt shown below.
Prompt for the LLM-based evaluator in GRPO
System prompt:
Compare the ground truth with the prediction from Al model and determine the prediction correct. The question about an image, which we have not given here. You need to determine whether the model's prediction is consistent with the ground truth. No points will be awarded for wrong answers, over answers or under answers. There are times when the answer may have different form of expression and some variation is acceptable.
User instruction prompt:
## Ground Truth: The correct answer is (answer).
(For multiple choice question: The correct option is (answer]: (option_content)
## Prediction: [final answer)
Conrectness: (Yes or No)
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H.3 INFERENCE
Prompt for inference
{query) Let's think step by step. Please analyze the question carefully and follow these re- quirements:
Provide detailed step-by-step reasoning,
Show all your work and calculations, End your response with one of these formats:
1. For choice questions: 'The answer is [option]'
2. For other questions: 'The answer is [final answer]'
The final answer line must be on its own line the very end of your response.
H.4 EVALUATION
Prompt for evaluation
System prompt:
Compare the ground truth with the prediction from Al model and determine if the prediction is correct.
The question is about an image. which we have not given here. You need determine whether the model's prediction is consistent with the ground truth. No points will be awarded for wrong answers, over answers under answers.
The reasoning process in the prediction does not need be considered too much, you only need to determine if the final answer is consistent. There times when the answer may have different form of expression and some variation is acceptable.
User instruction prompt:
## Question: {question]
## Ground Truth: The correct answer is (answer).
(For multiple-choice question: The correct option is (answer]: [option_content)
## Prediction: _anal_answer]
You need determine whether the model's prediction is consistent with the ground truth. Output only:
Correctness: (Yes or No)
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1 CASE STUDY ON MODEL PERFORMANCE ON GAMEQA
We present GPT-4o case studies on different GameQA games below, to showcase deficiencies of GPT-4o on visual perception and reasoning. Our qualitative analysis of model performance across four game types reveals common behaviors and challenges.
Question: What is the correct height relationship between the three numbered points?
Answer: 1 <3
GPT-4o Response: Point is top cube that one level above the blue starting cube. Point 2 is on the same level as the blue starting cube ... Therefore, the height relationship is: <1 <3.
Error nalysis: Point at same height as point
Figure 12: GPT-4o 3D Maze case study
Reconstruction Game
Question:
Goal: Reconstruct 3D structure by adding voxels
match given projections. Position (x.y.)) ranges from 1 to with (1,1,) front left bottom. All voxels must be connected face-to- poce-to-aaee face. New voxels can only be placed adjacent to existing ones. Choose the
Vew (Z Plano)
Current Structure
correct sequence from the options below.
ptions:
dd voxels at positions: [(1, 3, 1), (3, 3, 1)]
Answer:
To solve this puzzle, need to add voxels the current structure such that the pmjections match the given Front View
PT-44o Response:
Side View (X Renc)
(Y-Z) and Side View (X-
Let's analyze the projections:
1.Front View (Y-Z Plane):
The projection shows a filled
(2, 1), indicating there least voxel along the line of sight =2 and z=1.
Remm King autiichie woyile
2. The projection shows a filled cell (3, 1), indicating there at least one voxel along the line of sight at =3 and 2=1.
2
2.Side View (X-Z Plane):
1. The projection shows filled cells at (1, 1), (2, 1), and (3, 1), indicating there at least one voxal along line sight
x=1, x=2, and x=3, all a=1.
Given the current structure, add two voxels to match both projections.
have voxel at (2, 1). We need to
Error A nalysis: GPT-4o gives wrong position of the existing voxel.
Figure 13: GPT-4o 3D Reconstruction case study
Question: How many cubes are there in total in the scene?
Answer: 48
GPT-4o Response: The scene shows cube composed of smaller cubes. By counting the cubes along one edge, we can determine the dimensions of the larger cube. There are 5 cubes along each edge. Therefore, the total number of smaller cubes in the scene is calculated by cubing the number cubes along one edge: 5 x 5 125, So, there are 125 cubes in total the soene. Ground Truth: Looking at the top of know that length and width are 4 and 4 respectively. Besides, can see that the height Therefore, xx = 48. The answer is 48.
number cubes is 4
Error Analysis: GPT 4o counts number of cubes in each edge wrong.
Figure 14: GPT-4o Minecraft case study
3D Spatial Perception and Understanding. Visual language models exhibit significant limita- tions in 3D spatial reasoning games and score lowest among the four game categories. For example, in Figure I2 GPT-4o struggled on ordering green cubes by their height (Z-coordinate), recognizing cube 1, that is closer to top of the image, as having higher Z-coordinate than cube 2. Moreover, in Figure I. GPT-4o failed to identify the position of the only voxel in the image. In Figure [[] GPT-4o appeared to be unable to count how many cubes are on each edge of the cuboid, which is generally easy for humans.
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Pattern Recognition and Matching. In this game category, find that models faced difficulties in identifying patterns and locating objects. This was particularly challenging with non- layouts or images without row and column indicators. As seen in Figure IS GPT-4o has poor performance in circular Zuma grid, claiming to find pairs of same color marbles, but none is correct. GPT-4o also meets problems in the card games. In Figure IIG, GPT-4o fails to understand "top of pile" appears lower in the image. In Figure IA GPT-4o misreads the rank of the card.
Question: How many marble groups of two or more same- colorco marbles are there at the up-right side of the frog?
Answer:
GPT-4o Response:
Starting from the top, the first group in the up right region is a group two yellow marbles, Moving clockwise, the next group is a group of two blue marbles.
Continuing further, there is a group two red marbles ... there are a total of groups. Error Aallsis:: None of the three groups GPT-40 claims correct.
Figure 15: GPT-4o Zuma case study. The red rectangle is added to highlight the area referred to in question and does not exist in original image.
FreeCell
Question: In this FreeCell game, we have cascade piles at
sum, and their indexes are [0, 1, 2, 3, 4, 5, 6, 7J. FreeCell, cards van moved according specific rules: card can be moved a free cell available, stacked in descending order altemating colors in cascade piles, placed in foundation piles starting from Ace. Now, find the 3rd card from the top of cascade pile Options are as follows:
1.(Diamond, 5) 2,(Club, (Spade,, 6) (Heerr, 2) 5.(Club, 4) 6.(Heart, K) 7.(Heut, A) (S(paee 9)
nsswr:
To find the 3rd card from the top of cascade pile 1, we need to look the cards in that pile. The cards cascade pile 1, from top bottom, are:
GPT-4o Response:
Hearts of Hearts A Clubs. Thersfore, the 3rd card from the top of cascade pile the Ace of Clubs. The correct
option 2. (Club,
Ground Truth: The selected pilet contains (from top to bottom) the following cards: (Spade,5), (Heart,3), (Spade,6) 6). (Spade, Q), (Club,A), (Diamond, 10), (Heart,5). The 3rd card from the top is (Spade, 6).
Error A nalysls: GPT-4o does not understand top of pile appears lowest image.
Figure 16: GPT-4o Freecell case study
Multi-step Reasoning. Models have substantial deficiencies in multi- reasoning. When a rule needs to be applied multiple times recursively, models are prone to repeat the initial application instead of performing subsequent reasoning steps. In Figure [T GPT-4o repeats it finds prefixes of the target word "JUNKMAN" in options and 8, but actually the three options are far from the word "JUNKMAN". Another common flaw is failure to track game state changes. For example, in 2D Turing Machine game (Appendix (,S)), models frequently forgot the symbol on a cell that had been modified in previous step, still using the unchanged symbol to deduce the next move. A similar phenomenon was also observed in Langton's Ant game (Appendix L.3.3), where the model might not follow the new color of a certain cell that it had correctly recognized, still following the original cell color the input image.
Strategic Planning. These games show that models lack certain abilities to find the best strategy. They lack the human-like insight to prune unpromising choices, and are also unable to conduct large- scale search or traversal, resulting in poor performance. In the Sokoban game case (Figure 2T), for example, the number moves needed 27, a relatively big number. GPT-4o directly states the answer is 29, without conducting any effective analysis. Irrational mistakes that seems more unreasonable also exist. As shown in Figure 2T, GPT-4o states that after the first step. body of snake includes (0, 3) and new head position is (0, 3), not realizing that this overlap means the snake has hit
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Question: Piles numbered from left right starting with '0'. Cards in a pile are numbered from on scrtci top to bottom starting with '0", Cards can be moved another pile as long as they form a descending sequence of the same suit. The cards within each waste pile are also numbered starting from the bottom card. What will heppen if want to move the number card of pile to pile 3? Options: A. move be successful, and the cards will be descending order, following the rules movement, B. The move cannot be made because this card is face- down and its value is unknown, C. The move cannot be made because Answer:
GPT-4o Response: In the image provided: Waste pile 6 has a visible card, which is Jack ()) of spades. Waste pile 3 has visible card, which King (K) spades. The number card in pile 6 would the image, pile only has bottom not visible and is face-down. Given this information, the correct option is: The move cannot be made because this card is face- down and its value is unknown.
sixth card from the hottom of the pile. However, in face-down, Therefore, the sixth card from the
fare-up card, the Jack spades, and the
Ground Truth: Source pile is the 7th pile from the left, and destination on pile 3 is the 1th pile from the left. The number 6 card in pile 6 is the 7th card, which Queen Spade. The top card of pile King of Spade, So moving the Queen Spade from pile 6 will be successful it forms valid descending sequence.
Error Analysis GPT-4 recogiizss the visile card pile incorrectly and miscoad the meaning of number card.
Figure 17: GPT-4o Spider Solitaire case study
This Minesweeper game. The size the chessboant is 6x6,, and there are a total of mines hidden on board. The numbers on the board indicate how many mines adjacent to cell, including diagonals. Cells marked with "F" (flagged) are identified as potential locations of mines based on logical deduction prior knowledge. Question: How many mines are left to be found?
Question:
Minesweeper Board
1
2
5
Answer: 6
0
1
1
F
GPT-4o Response: To determine how many mines are left to be found, need to analyze the board and the flagged cells. 1. Count the flagged cells: There are three flagged cells on the board: (0,5), (1,0), and (0,0). Total The problem states there are total of mines on the board. Total mines =8 Flagged cells =3 Mines left to be found = Total mines Flagged cells
2
1
mines:
2
2
Ground Truth: There are total 8 mines hidden on the board. By counting the number of F-marked cells total of 2), can determine the remaining mines: Error Analysis GPT-4o incorrectly states (0,0) is flagged.
3
4
2
2
1
1
Figure 18: GPT-4o Minesweeper case study
6
Question: Find word "JUNKMAN" in the grid. W here does start and in which direction does go? Options:
S
J
C
B
I
Answer: Row 7, Column 4. Direction: up
2
N
1
T
4
Z
H
F
GPT-4o Response:
Option 2: Row 1. Columa 2, Direction: down. Starting at (1. 2) 2),
(2, 2) = UU, (3, 2) 'N', (4, 2) = K', (5, 2) MM', (6, 2) = (7, =N This spells
E
K
M
G
K
JUNKMAN: so this option correct.
G
P
Option Row Column Direction: singonal-right-dwwar Starting 2) JT, (2, K; (5, 6) ='M', (6. ='A'
=UU' (3, 4) N',
M
V
A
K
Option B: Row 7, Column 4. Direction: diagonal left-up Starting (7, 4) =JJ' Diagonal-
P
B
P
left-up: (6, 3) = 'U", (5, 2) =NN, (4, 1) = '''
Error A Analysis: G GPT-4o is hallucinating that the three options listed have letters similar to "JUNKMAN".
Z
C
X
Q
Optio 2 Option
Option 7
GPT-4o Word Search case study. Red rectangles and "Option X" texts are added to Figure 19;
highlight the options and do not exist in original image.
its head. This reveals certain deficiencies in models' fundamental reasoning and spatial imagination skills.
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Question: What is the minimum number moves needed to sulve this puzzle?
Answer: 27
GPT-4o Response: solve this Sokoban puzzle, player needs push the box the largt. The player must navigate around the walls and push the box in way that it reaches the target with the minimum number of moves.
After analyzing the puzzle, the minimum number of moves needed solve is 29.
Error Analysis: GPT-4o states answer without analyzing.
Figure 20: GPT-4o Sokoban case study
In addition, it's worth mentioning that a bit counter-intuitively, models actually face major dif- ficulties in visual perception under various game scenes, as shown in Figure 2 IS. 23 and 24 constraining their final performances.
3
This is Snake game. The yellow block is the head of the snake. The blue block is the body of the snake, The red block is the food. The coordinatos (x, y) in the gnid represent the matrix format, where now index and y is the column index. The origin (0,0) is
Question:
0
the the upper snake moves like each stop: step ... Options: The snake the bound of the gri.. 1: The snake hits its body. 2: The snake reaches the food. 3: Nothing happens.
left of the grid. Which will happen
1
2
Answer: 1
GTT-4o Response: To determine what happens when the smake moves according to the given steps, let's track the position of the snake's head and body through each step: Initial position: Head: (0, 4)- Bady: (0, 3), (0,
2). (0, 1), (1. 1). (2, 1), (2, 2), (2, 3), (1, 3), (1, 2) Step 1: Move left- Hoad moves Lo: 10, 3) New body: (0, 4), (0, The snake grid.
bound of
4
Ground Truth: At first the snake head(yellow block) is (0, 4). The snake body blocks) is [(1, 4) (2, 11). Then moves like this: step move left Before moving, the snake is at [(0, 4) ... (2, 1)). fter moving left, the snake head will move left directly, which will be at (0, 3). Each block of the snake body will move to the position of the block in
front of it, so the snake body will be at [10, 4) (3, 1)). Now the snake hits its body.
Error nalysis: GPT-4o does not correctly identify cells snake body, does not realize the snake hits body, though it states step head moves to and new body includes (0,
Figure 21: GPT-4o Snake case study
On 3x3 board, there levels (0 to 2). each level k. the and y coortinates range from 0 to 2-k, resulting in (2-k)==2 slots per level. Two players are desi gnated as PLAY ER 0 and PLAY ER 1, with blue balls for PLAY ER and red balls for PLAYER What is the stalus of the ball on Level 1, which has coortinate ([1, 1))
Question:
Options: 1. PLAY ER 0 PLAY ER 3. Emply 4. Index out of bound
A Asswer: 2
GP Response: The image shows that there is no ball placed at coordinate (1, 1) Level 1. Therefore, the correct answ er is: 4. Index out of bound
Based on level I's grid (specifically at coordinate [1. 1]), the ball is red, which corresponds to PLAY ER 1.
Ground Truth:
Error A Analysis: (1, 1) on Level 1 is marked as red, but GPT-46 4- reads it empty, and wrongly thinks that empty fits option 4.
Figure 22: GPT-4o Pyramid Chess case study
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Question: Which directions are available to move now? Options: A. up B. left C. right D. up, down left, right up, left, right G. down, left, right H. up, down, left. right
2
3
Answer:
G GTT-40 Response: Up 1): This is blue block, the player cannot move up. Down (2,
FLC
1): This white block, so the player can move down, Left (1, 0): This is blue block, the player cannot move left. Right (1, 2): This is white block, so the player can move right.
3
Therefore, the available directions for the player to move are down and right. The correct option is: D. up, down
5
6
Ground Truth:
7
The player (1, 1), and for cells adjacent, only emply. The player er right. Therffor,, The option is C.
8
Error A Analysis: GPT-4o incorvetly recognizes (2, 1) as white.
Figure 23: GPT-4o Maze case study
Question: in game, board divided into 3 squares (called Nine-grids). We use (i, row, col) represent the coontinates (i. represents the coordinates of the Nine-grid; (mw, col) represents the coordinate of the cell within the Nine grid; j. row, col all range from to 3. Two players take turns placing pieces on the board to mark the cells on the board, with the first play or using "X and the second play er using "O" (this is the same as traditional TicTaaToe) A the gume starts, the piece in any
cell:
first player places
middle (i.., the Nine-grid (2, 2)). How many middle cells the image marked? Options: 0. 3. 2..33.5 6. 7. 6.6 7 79.8 10.
Nine grid
2
Answer:
GPT-4o Response:
Top-left grid: (2,2) is unmarked. Top-middle grid: (2,2) is unmarked.
Top-right grid: (2,2) (marked). Middle-left grid: (2,2) (marked),
Center grid: (2,2) unmarked. Middle-right grid: (2.2) unmarked.
Bottom-left, grid: (2,2) (marked). Bottom-middle grid: (2,2)
2
unmarked. Bottom-right right grid (2,2) is (marked).
3
Correct answer: 4
Ground Truth: By analyzing the blocks in the picture, we can see that the middle cells of nino- grid (1, 2) (Top- (To-midddle and (3. (Bottom- left are marked, while the rest are emply. So, are middle calls marked. which means answer
Error malyyis GPT misjudges the middle cells of fop-middle, Top- right, Middle-left, Bottom-right right, and Bottom-right.
Figure 24: GPT-4o Ultra Tic-Tac- Tac-Toe case study
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J DETAILS ON THE 30 GAMES AND MORE EXAMPLE DATA SAMPLES IN THE GAMEQA DATASET
A total of 10 games are introduced below in detail, with detailed question information and specific example QAs provided, while the others games brief.
Typically. each game provides example images for three Plot Levels (Easy, Medium, Hard) repre- senting different image complexities, along with their grading criteria.
For demonstration purposes here, the images have been uniformly scaled. Refer to the dataset repository for the actual relative sizes and resolutions of the images. The average height and width of the images in our dataset are presented in Section B
For the 10 games introduced in detail:
1. The specific questions uses these images as visual input.
2. Labels such as "E1", "M2", and "HI" are used to denote specific images. For example, "Q1 (E1)" indicates that the corresponding image for this QI question sample the "E1" image (i.e., the first image of the "Fasy" Plot Level).
3. For each game, its Introduction text is a common component prepended to the beginning of every associated question.
Due to space limitations, we reasonably simplify the Introduction for some games, and we also omit some information in some of the analyses. However, most analyses remain detailed and clearly demonstrate the reasoning processes.
J.1 3D SPATIAL PERCEPTION AND UNDERSTANDING
J.1.1 3D RECONSTRUCTION
The game takes place 3x3x3 three-dimensional space with randomly initialized small cubes (voxels). Players reference two target side views (projections) and continue placing voxels in the 3D space make the structure match these views (not considered some tasks), with maximum limit on placed voxels, all of which must be connected (not placed in midair). Question types include counting voxels in the current structure, identifying given coordinates contain a voxel, checking if the current structure matches the side views, predicting side views after voxel additions, selecting the addition sequence that results in the structure matching side views, and calculating the minimum voxels needed to be added from the current structure meet the two side views. The difficulty (Plot Level) primarily determined by the number of voxels in the target three-dimensional structure. Images and Plot Level division
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Easy Final voxel count [3, 5]
Medium Final voxel count [6, 10
Hard
Final voxel count [11, 15]
1D Vesel Recorstruction Garne
3D Vosel Reconstrction Garre
30 Visi Reconsmuctoo Gamee
sen Stuth
Oument Seucture
Condless Se
MI
H1
3D Wovel Recorstruction Garre
30 Vowel Recorstruciion Garre
30 Vowel Reconstrcton Game
Sercture
Seaeito Rend
E2
M2
H2
Question information
QA type
QA Level
Description
QI
Target Perception
Easy
Count voxels the 3D structure.
Q2
Target Perception
Easy
From options, select the position containing voxel.
Q3
Target Perception
Medium
Select the option describing how the structures projec- tions match target projections.
Q4
State Prediction
Medium
Predict projections after adding specified voxels. Choose the correct voxel addition sequence match target projection(s), adhering to game rules.
Q5
State Prediction
Hard
Q5 Strategy Optimization
Hard
Calculate the minimum additional voxels required to match both target projections.
Specific questions and analysis
Introduction:
The current structure has some initial voxels, and your goal is to complete it. Game Rules: 1. Goal: Reconstruct 3D structure by adding voxels match given projections.
2. Grid Space:
The game is played on a 3x3x3 cube grid.
Coordinates: Position (x,y.) ranges from 3, with (1,1,1) at front-leftbbottom.
4. Position Rule: Each position can contain at most one voxel.
5. Connectivity: All voxels must be connected face-to-face.
Voxel Limit: You have a maximum of n additional voxels available.
7. Placement Rule: New voxels can only be placed adjacent existing ones.
Front View (Y-Z): Shows structure when viewed along the negative X-axis direction (front to back), with Y as horizontal axis and Z as vertical axis. Projection coordinates are in (y,z) format.
Side View (X-Z): Shows structure when viewed along the positive Y-axis direction (left to right), with X horizontal axis and Z as vertical axis. Projection coordinates are in (x,z) format.
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Q1 (E1): How many voxels are there in the given structure?
Analysis:
The structure contains voxels the following positions: (2,1,1), (2,2,1). By counting these positions, we can see there are 2 voxels in total. Therefore the answer is 2.
Q2 (M1): Which of the following positions contains voxel? Choose the correct position from the options below.
Options: (3,2,2); 2: (3,2,1); 3: (2.3.1); (2.2,3); 5: (2.1,1); 6: (1.3.3)
Analysis: Let's analyze each option:
Option Position (3,222): This position is empty.
(omitted)
Option 5 Position (2,1,1): This position contains a voxel. This is the correct answer.
Option Position (1,3,3): This position empty.
Therefore, the correct answer is option 5.
Q3 (H1): How does the voxel structure's projections match with the target projections?
Choose the correct description from the options below. Options:
1: Neither Y-Z projection nor X-Z projection matches the target;
2: Only Y-Z projection matches the target; Only X-Z projection matches the target;
4: Both Y-Z and X-Z projections match the target
Analysis: Let's analyze the projections:
Looking along the negative X-axis direction (Front View, using (y.7) coordinates):
We can see voxels at positions [(2, 1, 2). ... (omitted), (3, 3, 3)], forming a Y-Z pro.jjjetnion
of [(1, I.. (omitted), (3, 3)] This matches the target Y-Z. projection exactly.
2. Looking along the positive Y-axis direction (Side View, using (x.z) coordinates): We
can see voxels at positions [(1, 1, I), ... (omitted), (3, 3, 2)1, forming a X-Z projection of
[(1, 1), (omitted), (3, 3)] This matches the target X-Z projection exactly.
Based on the above analysis, both projections match the target. Therefore, the correct
answer is option 4.
Q4 (E2): Action: Add voxels at positions: [(2, 2, 1)]
Question: After adding these voxels, what will be the X-Z projection of the new structure?
Answer Format:
1. Write the answer as a list of three lists: [[row]], 1.. [row2], [row3]] 2. Each row should contain three numbers or 3. Rows are ordered from top bottom of the projection 4. Numbers in each row are ordered from left to right 5. Use 1 to indicate presence of a voxel in the projection, 0 for empty space 6. Example format: [[O. 1, 0], [1, 0], [0. I]]
Analysis:
Let's analyze the projection:
Looking along the positive Y-axis direction (Side View, using (x.z) coordinates):
We can see voxels at positions [(2, 2, 1)], which in X-Z projection appear at positions
[(2, 1)). Therefore, the answer is: [[O, 0, O], [0. 0, 0]. [0. 1, 0]]
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Q5 (M2): Which sequence of voxel additions will make the structure match the both target projections?
Choose the correct sequence from the options below.
Options:
I: Add voxels at positions: [(1, I, 1), (1, I, 2), (1, 2, 1), (3,2, D)]; ... (omitted)
6: Add voxels at positions: [(1, 1), (1, 2), 1), (2, ... (omitted)
8: Add voxels at positions: [(2, I, 2), (2.3, (3.33)
Analysis: Let's analyze each option:
Current structure: [(2. 2, 1), 2,]
Option I: The added voxels maintain connectivity Does not match both target projections- Uses 4 voxels, which is within the limit of 4 (omitted)
Option 6: The added voxels maintain connectivity Matches both target projections Uses 4 voxels, which is within the limit of 4 ... (omitted)
Option 8: The added voxels are not all connected to the existing structure Does not
match both target projections Uses 3 voxels, which is within the limit of 4 Therefore, the correct answer is option 6.
Q6 (H2): What the minimum number of voxels needed to add the current structure make match both target projections?
Analysis: Let's solve this optimization problem through systematic reasoning:
1. Basic Information: Current structure: 6 voxels positions [(1, 1, 1), 1) (2,
1), (2, 1, 2), (3, 1, 1), (3, 2, 1)] Remaining available voxels:
2. Analysis of Y-Z Projection (Front View):
a) Current Y-Z projection: [0, O, OJ (top) [1, O, 0] (middle) [1, 1, 0] (bottom)
b) Target Y-Z projection: [1, 0] (top) [1, 0] (middle) [1, 1, 0] (bottom)
c) Candidate positions from Y-Z view: (?, 1, 3), (?, 2, 2), (?, 2, 3) where ? can be any
value from to 3 for x-coordinate
d) Note: At positions where projection already shows 1, we can add more voxels without affecting the projection. For example, if (2, y0, z0) exists (where yO and z0 are specific fixed values),
add (1, y0, z0) (3, y0, z0) at the same projection position.
3. Analysis of X-Z Projection (Side View): (omitted)
4. Finding Required Positions:
By matching candidates from both projections:
When (?, z) from Y-Z view matches (x, 2) from X-Z view, position (x, y, z) can be filled.
Example: we have (?, 2, 3) and (1, 3), then (1. 2, is required
To ensure connectivity, we can add voxels at positions where projections already show
This strategy is optimal because it doesn't create new projections
* Use these positions as 'bridges' connect required positions Required positions from projection matching: [(1, 1, 3). (2, 2, 2), (2, 3)]
5. Connectivity Analysis and Completion:
(omitted)
6. Verifying Optimality: ... (omitted)
Therefore, the minimum number of voxels needed to complete the reconstruction is 3.
J.1.2
3D MAZR
This game involves pathfinding within a three-dimensional maze constructed from unit cubes ar- ranged in a 3D grid space (voxel-bused). Traversal is subject to specific rules: horizontal movement (along X axes) is permitted between adjacent cubes only if they reside at the same height (Z-coordinate). Vertical movement (ascending/descending along the Z-axis) is permitted between vertically aligned cubes (sharing X and Y coordinates) only if a ladder explicitly connects them. Key locations are color-coded: blue cube designates the starting position, and red cube marks the goal destination. Additionally, green cubes, often labeled with numbers, serve as waypoints, decision junctions, specific points of interest referenced in the questions. Question types assess spatial navigation and path analysis: (1) determining the correct direction travel required at each green waypoint follow path towards the destination; (2) ordering set of specified green cubes
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based their height (Z-coordinate); (3) identifying the sequence of green cubes visited along the shortest path from the start to the end; (4) reporting the exact sequence in which green cubes are en- countered when traversing from start to end following defined path. Path generation often utilizes concatenation of randomized "atomic' path segments (e.g.. move +2X, move +2Y, move +2/37.) to create primary route, with branching paths potentially added similarly introduce choices, aiming to minimize visual occlusion between path segments.
Images and Plot Level division
Easy Simple, a single path
Medium
Complex, side road exists
E1
MI
E2
M2
Question information
QA type
QA Level
Description
Q1
Target Perception
Easy
Height Comparison
Q2 State Prediction
Medium
Sequence Finding
Q3 State Prediction
Medium
Main Path
Q4 State Prediction
Hard
Path Finding
Specific questions and analysis
Introduction: Rules: 1. Player can only walk on top of cubes
2. Player can climb ladders if they can reach the cube under the ladder
From ladder, player can reach the top of the last cube with the ladder
4. Blue cube is start position, red cube is goal position
Green cubes are numbered points (1, 2, and 3)
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Q1 (E1): What the correct height relationship between the three numbered points? Use '<' for 'lower than' and '=' for 'same height as'. Options:
1:
2: < < <24 2
<
6: 2< 1
=2
Analysis: Analyzing the heights of each point:
Comparing points and 2: Found path from 1 to 2:
Move left-forward * Move left-forward
Point is same height point 1
Comparing points and 3: Found a path from to I:
Go up 3 blocks Go up 3 blocks
Point is lower than point
Comparing points and 3: Found path from to 2:
Go up 3 blocks * Go up 3 blocks * Move left-forward Move left-forward
Poonnt 3 is lower than point 2
Therefore, the correct height relationship is 3 < 2, making the answer Option 8.
Q2 (E2):
What the correct sequence of numbered checkpoints when following the path from start to goal?
Options:
1: Start > 3 - -> Goal; 2: Start -> Goal;
3: Start ->4 11 Goal; Start >=-->->>>->> Goal;
5: Start
Goal; 6: Start - -> Goal
Analysis: Following the path from start to goal:
Step 1: Move right-forward Step 2: At checkpoint 2 2-eep Move up
Step 4: Move left-forwand
Step 5: At checkpoint 3 Step 6: Move left-forward
Step 7: Move up- Step 8: At checkpoint 4 Step Move right-forward
Step 10: At checkpoint 1 Step 11: Move right-forward
Therefore, the correct sequence is Start Option 2.
24 -> Goal, making the answer
Q3 (M1): Which numbered blocks are passed through when following the most direct path from start goal?
Options:
1: 1, 2: 2, 3; 2; 5: 1; 6: None; 7: 2, 3; 8: 1,3
Analysis: Following the main path from start to goal:
-Step I: Move up Step 3: Move up- Step 4: Move right-forward
Step 5: Move left-forward Step 6: Move right-forward Step 7: Move rorhttforward
Blocks not on main path: 1, 2. Therefore, the blocks passed through on the main path
are: 3, making the answer Option 3.
Q4 (M2): Which combination path choices leads to the goal? Options:
1: l-right-forward, 2-right-forward, 3-up;
2: 1-left-forward, f-rwgh,-forward, 3-left-forward:
3: 1-left-forward, 2-up. 3-left-forward; 4: 1-left-forward, 2-up, 3-up;
5: I-left-forward, 2-right-forward, 3-up; 6: 1-right-forward, 2-up, 3-up;
7: l-right-forward, 2-right-forward 3- f-lwar-forward;
8: 1-right-forward, 2-up, --lettffowwar
Analysis: From the start point, you first meet branch 1, then branch 2, then branch 3, before finally reaching the goal.
Analyzing each branch point:
At branch 1, going forht-forward leads to branch 2, while going left-forward leads to dead end
At branch 2, going up leads to branch 3, while going right- forward leads to dead end
At branch 3, going up leads toward the goal, while going left-forward leads to a dead end
Therefore, the correct sequence is i 1-right-forwart 2-up. 3-up, that is I 1-right-forware 2-up, 3-up, making the answer Option 6.
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J.1.3 RUBIK'S 's CUBE
This game is based on the classic Rubik's Cube puzzle. The game interface presents both 3D views and an unfolded view of the cube. The 3D views display the cube from two different angles: left- tilted 30 degrees looking down, and right-tilted 30 degrees looking up. The cube features six faces with distinct colors (yellow, white, orange, red, blue, and green), and players can manipulate the cube according to standard rotation rules (where B, I., R, U, represent Front, Back, Left, Right, Upper, and Down faces, with prime symbol denoting counterclockwise rotation).
Question types, assessing spatial reasoning and pattern recognition, include identifying the color at specific position a face, counting a color's s occurrences color after a move sequence. Further questions ask for the minimum moves to solve a single face or the entire cube. The difficulty level (Plot Level) is determined by the number of random moves used to scramble the cube: move for Easy, 2 moves for Medium, and 3 moves for Hard.
face, and predicting position's '
Images and Plot Level division
Easy
Medium random moves
Hard 3 random moves
1 random move
Uhe BLO. BTOI!!
ENCEEE GLO. Boot
J.1.4 PYRAMID CHESS
This is a 3D two-player competitive game. Players take turns placing balls on board, building a pyramid structure layer by layer. The player whose ball occupies the pyramid's top wins.
Question types challenge players to assess the board by determining which player's ball occupies a given position, the specific state of any board position, and the total count of balls. Additionally, questions involve predicting the result of a player placing a ball, calculating the minimum number of moves required to place ball at certain position, and identifying a player's 's optimal placement in the current state. Plot Level is determined by the board's 's base size, with larger base dimensions increasing the challenge.
Easy Level 03 x 3
Medium
Hard Level 0
Level 0 x 4
5
J.1.5 MINECRAFT
This Minecraft QA generator is designed to produce a series of questions that test 3D perception and understanding within simulated "Minecraft" environment. Given the open-ended nature of to probe specific cognitive abilities. The generated ques-
Minecraft, the tasks are tions aim evaluate how well an agent can interpret and reason about 3D scenes.
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The question set begins by assessing precise 3D perception. QI requires recognizing various scener- ies present in the image, such as different ores, TNT, pumpkins, or environmental features like rivers and lava. Q2 tests the ability accurately count the total number of blocks in given structure. These foundational perceptual skills are prerequisites for the subsequent three tasks, which demand reasoning based on both visual input and provided rules. These more complex questions involve planning: determining the minimum blocks to cross a river (Q3), calculating the blocks needed to reach target block certain height, possibly using ladders (Q4), and combined scenario requir- ing both river crossing and climbing to access a target block, again considering ladders (Q5). Plot Level is determined by the number of sceneries (Q1), the cuboid size (Q2), the width of the river (Q3, Q5) and the height of the target block (Q4, Q5).
For Q1
For Q3
For Q4
For Q2
For Q4
For Q5
J.2 PATTERN RECOGNITION AND MATCHING
J.2.1 COLOR HUB
This game involves reasoning about color gradients within a grid structure. Certain rows and/or columns within the grid display smooth color transitions. Cells that are intentionally left blank or empty are visually marked with a gray crosshatch pattern. Color information may conveyed using standard color names (e.g.. "purple"), derived programmatically from their (HSV) properties.
Naturration-Valus
Question types focus understanding and interpolating these color gradients: (1) identifying the specific color present at a given row and column index; (2) determining the starting and ending colors of specified gradient options (e.g., six color patches) that should logically fill a designated blank cell (marked with letter) based the surrounding gradient(s). The complexity ('plot level') scales with the dimensions of the grid.
column; (3) selecting the correct color from provided set of
Images and Plot Level division
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Easy
Medium
Hard
5 5
6 6
8 x 8
3
2
3
B
2
A
5
6
M
H
Question information
QA type
QA Level
Description
Q1
Target Perception
Easy
Color Description
Q2 Target Perception
Medium
Gradient Pattern
Q3 State Prediction
Hard
Color Matching
Specific questions and analysis
Introduction: Rules:
Each numbered region represents a piece on the board.
2. Pieces are considered adjacent if they share at least one edge.
Pieces that only touch at corners are not considered adjacent.
4. Some pieces have been removed and are shown below the main board.
Q1 (M):
What color is the cell 1, column 6?
Options:
I: green; 2: white; vivid red; 4: pale bright green; 5: bright cyan; 6: cyan; 7: bright orange; 8: dark green.
Analysis: The cell at position (1, 6) is pale bright green. So the answer Option 4.
Q2 (E):
What is the gradient pattern in column 5?
Options:
1: transitioning from bright purple to pale dark cyan;
2: transitioning from vivid green to pale bright purple;
3: transitioning from pale bright yellow to vivid dark blue;
4: transitioning from vivid bright blue to pale red;
5: transitioning from yellow to light gray;
6: transitioning from red to pale bright cyan;
7: transitioning from purple to bright red;
8: transitioning from black to pale bright cyan.
Analysis: The column 5 shows a pattern that is transitioning from purple to bright red. So the answer Option
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Q3 (H):
Which color should be put in cell B?
Options: Colors are numbered from to 6 in the palette below.
Analysis:
We need to find the correct color for cell B at position (2, 6). Let's analyze the color patterns around this cell:
Looking vertically, we see a pattern transitioning from pale bright red to bright yellow. Let's look at our color options:
Option is bright yellow; Option 2 is vivid dark purple: Option 3 is pale yellow; Option is vivid bright indigo; Option 5 is yellow; Option 6 pale bright red. Based on the pattern, we should use pale bright red (Option 6).
J.2.2 TANGRAM
This game presents a spatial reasoning puzzle inspired by Tangram, involving the manipulation and fitting of polygonal shapes within a grid. The grid is partitioned into several distinct regions or "pieces", each identified by a unique integer ID. Cells belonging to specific piece display that piece's ID number; cells not part of any displayed piece are left blank, representing empty space. One or more pieces are removed from the main board. Questions test pattern recognition and spatial matching skills across various dimensions: identifying piece area and adjacency, determining correct rotations to fit removed pieces back into empty spaces, and strategically positioning multiple pieces to fill available gaps. The puzzle complexity scales with the grid size.
Images and Plot Level division
Easy
Medium
Hard
5 x 5 Main Board
x 8 Main Board
x 9 Main Board
Tamoved Ploces
Ramoved Paom
E1
M1
H1
Main Ecard
Man Feard
Nan Doard
5.3
180
114
F2
M2
H2
Question information
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QA type
QA Level
Deseription
Q1
Target Perception
Easy
Main board piece
Q2 State Prediction
Medium
Removed piece rotation feasibility Target piece area calculation Adjacent piece type count Piece Placement
Q3 Target Perception
Medium
Q4 Target Perception
Medium
Q5 State Prediction
Hard
Specific questions and analysis
Introduction: Rules:
Each numbered region represents a piece on the board.
2. Pieces are considered adjacent if they share at least one edge.
Pieces that only touch at corners
considered adjacent.
4. Some pieces have been removed and are shown below the main board.
Q1 (E1):
How many pieces currently the main board?
Options: 1: 4 2: 1 3: 0 4: 5:
Analysis: Let's analyze the puzzle state:
Pieces currently on the board: Piece 2 (vivid bright orange) around position (3, 3) Removed pieces: Piece 4; Piece 3; Piece
By counting the unique non-zero numbers the main board, there pieces remaining. Therefore, the answer Option 2.
Q2 (M1):
Can the removed piece fit back into the main board by only rotation? If yes, what rotation(s) would work?
Options: 1: rotate degrees 2: matter what degrees rotated, it always can fit 3: both rotate 0 and 180 degrees 4: rotate 90 degrees clockwise 5: can't put inside (flipped) 6: rotate 180 degrees 7: rotate 90 degrees by both direction 8: rotate 90 degrees counterclockwise
Analysis: Let's analyze how piece can be rotated to the hole:
1. Dimension Analysis: Hole dimensions: 6x6 Piece dimensions: 6x6
Based on dimensions, these rotations (clockwise) might work: 0, 90, 180, 270
2. Testing Fach Rotation:
Of rotation: Failed: First mismatch at row 0, column (mapped to (0, 1) of removed
piece and (2,3) of board)- At this position, the hole was empty but the piece was absent (omitted: 907 and 180F rotation)
270ř rotation: Success! Piece fits perfectly
3. Summary: Valid rotations found: 270 Some rotations work
So, the correct answer is: rotate 90 degrees counterclockwise which is Option 8.
Q3 (M2): Question: What is the area (number of cells) of Piece 1?
Options: 1: 11 2: 12 3: 14 17 5: 18 6: 19 7: 20 8:
Analysis: Let's analyze Piece (vivid blue) row by row: The piece spans from row 0 to 5 (height of 6): Row 0: 2 cells from column 0 1; ... (omitted)
Adding up all the cells: 2 + +2 4 17 cells. Therefore, the answer is Option 4.
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Q4 (HI): Question: How many different pieces are adjacent Piece 3?
Options: 1: 5 2: 6 3: 0 4: 2 2:: 3 6: 4 7: 8:
Analysis: Let's analyze Piece 3 (green):
Piece Boundaries: Spans rows 3 to 7 (height: 5) Spans columns 4 to (width: 5) 1. Cell-by-cell examination: Cell (3.7); No adjacent pieces: (omitted) Cell (5,4):
down: Piece 4 (vivid bright red) at (6,4) ... (omitted) Cell (7,8): down: Piece (vivid
bright red) at (8,8)
2. Adjacent Pieces Summary: Piece (vivid bright red): 7 contact sides Total number of unique adjacent pieces: 1. Therefore, the answer is Option 8.
Q5 (H2): Question: At which position should Piece be placed? Each option shows hop_row,left col) to (bottom row,right col).
Options: 1: (0.3) (6,6) 2: (0,6) to (6,9) (0,4) to (6,7) (0.5) to (6,8)
Analysis: Let's analyze the placement of Piece and Piece
1. Hole dimensions: 7x5 2. Piece 1 dimensions: 7x4 3. Piece 4 dimensions: 4x4
We know that if Piece fits, then it must be placed at one of the four corners.
Testing each corner: upper-left: Attempting to place Piece 1 at (0.5) to (6,8) Failed:
Cell (4,0) on Removed Pieces plot maps board position (4,5) which isn' I'' empty upper-right: Attempting to place Piece 1 at (0,6) to (6,9) Success! Remaining hole dimensions: 4x4 Then placing Piece at (0,5) to (3,8) Both pieces perfectly!
bottom-left: Since Piece has the same height the hole, bottom-left corner is same upper-left corner. Skipped. (omitted: bottom rrght, same as upper-right corner) Therefore, Piece should be placed at position (0,6) to (6.9) shown in Option 2.
J.2.3 FREECELL
This scene presents solitaire card game whose goal is to move all cards to foundation piles, fol- lowing specific rules. This game is played with a standard deck of 52 cards, arranged in tableau columns, four open cells, and four foundation piles. Cards can be moved between tableau columns according to descending order and alternating colors, while empty tableau spaces can only be filled by kings. The four open cells act as temporary storage, allowing players to temporarily hold cards for strategic moves. Complexity controlled by adjusting the number of tableau columns, testing the model's ability to search for efficient operation list to complete the game.
Easy 8 cascade piles
Medium
Hard 4 cascade piles
cascade piles
TreeCall Corr Stare
Cue Ceme Stane
Presout Came Stnte
J.2.4 TETRIS
This detris-derived game maintains the original objectives while simplifying visuals to highlight core information. Players arrange falling blocks to eliminate rows by: moving/rotating pieces during descent until they land the bottom or on other blocks, clearing complete horizontal rows. The game ends when blocks reach the grid's top. The simplified interface shows white grid with gray squares representing placed blocks and red squares indicating the current falling piece (with grid coordinates). While actual games use color-coding for different block batches, this visual distinction omitted as irrelevant to gameplay logic. Advanced Tetris variants are excluded.
Questions cover: 1) Empty squares in specified row 2) Identifying the current red block's shape 3) Timestamps until the falling block lands after given moves 4) Maximum eliminable rows from the current block's optimal placement.
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Easy 8 8
Medium
Hard 16 x 16
12 12
10.01
10.01
J.2.5 ZUMA
This game is classic marble-shooting puzzle game where players control frog that shoots colored marbles toward chain of rolling marbles on a track. The objective to clear all marbles before they reach the black hole the end. Players must create groups of three or more same-colored marbles, which will disappear from the track. The frog's marbles travel in a straight line until they hit marbles already in their path.
The game tests spatial reasoning, color recognition, and strategic planning through various question types: identifying the color of the next marble be shot, counting marbles of specific colors, de- termining the number of same-colored marble groups in certain directions, predicting which marble will be hit at specific angles, analyzing the outcome of shots, and evaluating optimal elimination strategies. Plot difficulty levels are determined by track length and marble count.
Easy Short track with few marbles
Medium
Hard
Medium-lengle track with more marbles
Long track with many marbles
J.2.6 SPIDER SOLITAIRE
The game is based on Microsoft's classic Spider Solitaire, with the original four suits simplified to just one suit. The objective of the game to move all 13 cards of the same suit, arranged in descending order from King to Ace, from the waste piles to the foundation piles. The cards in the waste piles must be arranged in descending order. The foundation piles serve as the final destination for complete sequences. The game screen includes several waste piles, stock pile, and foundation piles, with each pile containing several stacked cards. Some cards are face down, indicating that face up, revealing their rank. The dataset includes tasks
their rank is unknown, while others such as identifying the card on top of pile, moving cards from the waste piles, and determining the optimal move. The dataset is divided into three difficulty levels based the number of waste piles.
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Easy 8 waste piles
Medium 9 waste piles
Hard 10 waste piles
J.2.7 JEWEL2
Jewel2 is grid-based strategic puzzle game. It is inspired by Microsoft's classic game Bejeweled 2, with certain modifications made the original game. The game board is square-shaped and consists of five basic elements and seven special elements. The basic elements are five gems of the same shape but different colors, while the special elements are seven gems with different shapes from the basic ones, designed to test the model's pattern recognition ability. The main objective of the game is to eliminate elements by forming horizontal or vertical lines of three or more identical items. Successfully eliminating elements increases your score and clears space for new elements to appear. The game tasks include recognizing elements on the board, executing climination operations, and maximizing the score. Plot Level is determined by the size of the board
Easy
Medium
Hard
4 4
5 x 5
6
Jewel2 Game
Jewel2 Game
Jewel2 Game
Total Cleared: 44
Total Cleared:
Cleared:
J.2.8 KLONDIKE
This Klondike colitaire-based strategy game challenges players to analyze card layouts and apply rules for optimal decisions. uses standard interface with Stock, Waste, Foundation, and Tableau piles. The goal is to move all 52 cards, by suit and in ascending order (Ace to King), to the four Foundation Piles. Key mechanics include building Tableau piles down in alternating colors and descending order, building Foundations up. and strategically moving cards reveal face-down ones, utilize the Waste Pile, and advance cards to Foundations.
Questions, generated from the current card layout, cover diverse Klondike decision-making and analysis scenarios. Types include identifying valid moves, determining the most effective move strategy (e.g., one that reveals card or helps build Foundations), and analyzing for deadlocks. Players must apply logical reasoning based on on-screen card information and Klondike rules to select correct answers. Difficulty is dynamically set by the number of face-up cards.
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Easy #face-up cards < 19
Medium #face-up cardse [20, 23]
Hard
#face-up cards > 24
J.3 MULTI-STEP REASONING
J.3.1 STAR BATTLE
This presents 2D (E matrix which are divided into regions. Each region has specified color and connective. The goal is to place stars in the matrix to make sure each row, col, region has only
and the stars must not be adjacent each other rows, columns and diagonals. Complexity is controlled by adjusting the matrix size, testing the model's ability to reason according the known rules.
Images and Plot Level division
Easy
Medium
Hard
5 x 5 (5 colors)
6 x 6 (6 colors)
8 x 8 (8 colors)
E1
M1
H1
F22
M2
H2
Question information
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QA type
QA Level
Deseription
Q1 Target Perception
Easy Easy
Identify the cell belonging to the given region Identify the cell belonging to the given region and contain- ing star
Q2 Target Perception
Q3 State Prediction
Medium Hard
Identify the cell where star can be placed Find the position of the final star needed to complete the puzzle
Q4 State Prediction
Specitic questions and analysis
Introduction:
We have a 5*5 grid. The grid divided into 5 regions. Cells with the same color belong to the same region.
Colors: RegionO (light pink), Regionl (powder blue), Region2 (light green), Region3 (peach), Re- gion4 (red), Region5 (yellow), Regionó (cyan), Region7 (orange).
In the image, star is represented by black dot. If a cell has been placed a star, a black dot will be shown on this cell. We should place the in this Star Battle Puzzle according to the following rules:
Each row must contain exactly 1 star(s). Each column must contain star(s). Each region must contain exactly star(s). Stars cannot be adjacent to each other, including diagonally.
The cells in the grid are labeled with row and column numbers starting from The top-left corner of the grid is (0, O). (x.y) means a cell at row x and column Now we have placed some stars in the grid.
Q1 (E2): The region with index is represented by the color powder blue in the grid. Given the current state, which cell the following options belong region Options:
1. (2, 3): (0, 3. 3. (3, 4. (3, 1);
5. (2, 2); 6. (3, 2); 7. (2, 1); (1
Analysis: The region with index 1 is represented by the color powder blue in the grid. In this
puzzle, we need identify which cell in the following options belongs to this region. The region 1 contains the following cells: (0, 0), (0, 1), (0, 2), (1, 1), (1, 2). So (0, 2) belongs to region 1. The answer is Option 2.
Q2 (E2): In the current puzzle state, region is associated with color powder blue. Please identify which of the following cells this region that contains a star? Options:
1. (1, 1): 2. (2. 3. (0, 1); O); 0):
5. (0, 2); 6. (4, 4): 7. (1, 2); (1,
Analysis: In this task,
need to find all the stars in the region with index 1. The region with index corresponds to the color powder blue. This region contains the following cells: (0, 0), (0. 1), (0, 2), (1, 1), (1, 2). Note that star is represented by black dot. Now scan the cells of the region on the image. The cell with a black dot is: (0, 1). So the answer is Option
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Q3 (MI): Now we have placed some stars in the grid. Based on the current puzzle state, which of the following cells can star be placed in?
Options:
1. (4, 2); 2. (5, 3); (1, 1); 4. ();
5. (1, 0); 6. (3, 4), (3. 3); 8. (4.
Analysis: Cell (3, 3) cannot hold star because: is adjacent to a star, so it cannot hold star.
Cell (4, 2) cannot hold star because: It is adjacent to a star, so it cannot hold star. Besides, this cell in region 2, which already contains one star, so cannot hold a star.
Cell (1, 0) cannot hold star because: It is not adjacent to any star. However, this cell is in region 3, which already contains one star, so it cannot hold a star. Cell (2, 2) cannot hold a star because: It is adjacent to star, it cannot hold a star. Cell (5, 3) cannot
hold a star because: It is not adjacent to any star. This cell is in region 1, which contains stars. However, Column has already been placed star. Therefore, it cannot hold star. Cell (1, 1) cannot hold a star because: It is not adjacent to any star. However, this cell is in region 3, which already contains
cannot hold star. Cell (3, 4) cannot hold star because: It is not adjacent to any star. This cell is in region O, which contains no stars. However, Row 3 has already been placed star. Therefore, cannot hold a star. Cell (4, 0) can hold a star because: It is not adjacent to any star. This cell is in region 4, which contains no stars. Both row 4 and column 0 now have no stars. Thus, the correct answer is Option 8.
star,
Q4 (H1): Now the puzzle has only one star left be placed. The left star should be placed in which cell?
Analysis:
** *Ste--by-step reasoning to solve the puzzle:**
** **Preplaced stars and their positions:**
The following stars are already placed: Row 1, Column O, Row 2, Column 3, Row 3,
Column 7, Row 4, Column Row 5, Column 1, Row 6, Column 4, Row 7, Column 6.
These positions fulfill the requirement of placing one star per row, column, and region. 2. Identify rows and columns with and without stars:
**Rows with stars: ** Row 1, Row 2, Row 3, Row 4, Row 5, Row 6, Row 7,
Rows without stars: Row O.
**Columns with stars:** Column 0, Column 1, Column 3, Column 4, Column 5,
Column 6, Column
** Columns without * Column 2.
3. ** Determine remaining valid cell:**
The final star must be placed in a row and column that are both missing stars. Based on the information above, the row without star is Row 0 and the column without a star is Column 2.
The only available intersection is cell (0, 2), which satisfies the row and column constraints.
4. *eegion check:**
The preplaced stars occupy the following regions: 0, 1, 3, 4,
The remaining region that requires star is: Region 7.
5. **Final validation:
The cell (0, 2) belongs to the remaining region without star. Placing the star here satisfies all row, column, region, and adjacency constraints. Thus, the final star must be placed at **Row 0, Column 2**
J.3.2 SUDOKU
Sudoku puzzle that requires filling a grid such that each row, column, and subgrid contains all digits from I to 9 without repetition.Our Our Sudoku-like puzzle game can be adapted serve multi-modal dataset by replacing the numbers 1-9 with nine different colors. this game, players provided with grid, where each row, column, and subgrid must contain all nine colors without repetition.
The types of questions in the game are as follows: 1.The color of specific cell. 2. The number of cells of a certain color on the board.3.The number of rows, columns, or blocks with more blank cells than specified number.4.1 The number of possible color options for specific cell under the current
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board conditions.5. The color for third cell after two other cells filled with specific colors. The difficulty level of the game is determined by the size of the grid and the number of filled cells.
Images and Plot Level division
Easy
Medium
Hard
4
most cells filled
9 x 9, fewer cells filled
E2
M2
H2
Question information
QA type
QA Level
Description
QI Target Perception
Easy
Position color identification Color occurrence count Sparse unit count
Q2 Target Perception
Easy
Q3 Target Perception
Medium
Q4 State Prediction
Medium
Valid color candidated inference Guided
Q5 State Prediction
Hard
Guided-position position color deduction
Specific questions and analysis
Q1 (E1):
What color is at position (2,1) (note that on the board the position (2,1) has already been filled with certain color)? Choose from the following options: A.red, B.green, C.blue, D.magenta
Analysis: From the image, we can see the color at Position (2,1) is red.
So the answer is A.
Q2 (M1):
How many times does aqua appear on the board? Color aqua appears at: (1,1), (2,9), (3,6), (4.7), (5,3), (6,4), (7.8), (8.2), (9.5,, total 9
Analysis:
times.
So the answer is 9.
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Q3 (E2): How many columns have more than empty cell?
Analysis: Col analysis:
col has 3 empty cells in positions 1, 2, 3:
col 2 has 2 empty cells in positions 1, 4;
col 3 has empty cells in positions 2;
col 4 has empty cells in positions 1, 4. In total, 3 col(s) have more than empty cell.
the answer 3.
Q4 (HI):
How many colors can be filled in position (7,1)? Infer based on the current situation focusing only on the colour of the position.
Analysis: Constraint anlysis for position (7.1):
Existing colors in row: purple, aqua, forest green, gray, yellow, red, green
Existing colors in column: blue, aqua, purple, red, green, yellow, forest green, purple Existing colors in box: purple, aqua, green, yellow, gray, blue
Therefore, possible colors are: magenta. So the answer
Q5 (H2): After determining colors at positions (2,1), (2,5), what color should be at position (2,4)? Choose from following options: A.red, B.green, C.blue, D.magenta, E.yellow, Faquu. G.gray, H.purple, I.forest green
Analysis:
Deductive reasoning process:
Step 1: Position (2.1): Existing colors in the row: green, aqua, gray, purple, blue, red. Existing colors in the column: purple, blue, aqua, gray, yellow, magenta. Existing colors in the 3x3 box: purple, gray, magenta, green, aqua, yellow, blue Therefore, the only possible color for this position is forest green. Step ... Therefore, the only possible color for this position is magenta. Final analysis for position (2,4): After previous deductions, possible color reduced to: yellow
So the answer is E.
J.3.3 LANGTON'S ANT
This game simulates the behavior of Langton's Ant in a cellular automaton. The represented by red arrow indicating its initial position and direction. It moves randomly generated grid composed of black and white squares, following fixed set of rules: If the ant on a black square, it turns 90 degrees to the right, flips the square to white, and moves forward one step; if the ant is a white square, turns 90 degrees to the left, flips the square to black, and moves forward one step.
There three types of questions in the game: 1. Identify the ant's initial position and direction. Predict the ant's 's position and direction after a given number of steps. Given a specific square, infer how many times its color has changed after the ant has moved certain number of steps.
The difficulty of the game is determined by the question type and the size of the grid: the three question types increase in complexity respectively, and the grid size defines the level of difficultyn = indicates an easy level, while = 13 indicates a hard level.
Images and Plot Level division
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Easy 5 5
Medium 9 9
Hard 13 x 13
M
Question information
QA type
QA Level
Description
Q1 Target Perception
Easy
Identify the current position and direction of the ant.
Q2 State Prediction
Medium
Predict the ant's 's position and direction after several steps.
State Prediction
Hard
Count how many times specific cell changes its color.
Specific questions and analysis
Introduction:
In Langton's s Ant, we have a grid where each cell is either white or black. red arrow represents an ant, showing its current position and direction. The ant follows these simple rules:
1. If the ant is on white cell, it turns right 90 degrees, changes the cell to black, and moves forward step
2. If the ant is on a black cell, turns left 90 degrees, changes the cell to white, and moves forward one step
3. If the ant would move off the grid, it wraps around to the opposite side (using modulo with grid size)
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Q1 (E):
What is the current position and direction of the ant in the image?
Answer using one of the following options with its corresponding letter:
A: Position (1, 3). facing up: B: Position (0, 4), facing left
C: Position (2, 3), facing down; D: Position (4, 3), facing up
E: Position (4, 3), facing down; F: Position (0, 0), facing up
G: Position (0, O), facing right; H: Position (4, 3), facing right
Analysis: Step-by-step analysis:
1. Look at the red arrow in the image which represents the ant. The arrow's position indicates the ant is at coordinates (0, 0). 3. The arrow's direction shows the ant is facing right.
Therefore, the ant's ss current position is (0. 0) and it's facing right. The answer is G.
Q2 (E):
After 6 steps, what will be the ant's position and direction?
Answer using one of the following options with its corresponding letter:
A: Position (2, 0), facing down; B: Position (2, 3), facing right
C: Position (0, 0), facing right; D: Position (4, 1), facing right
E: Position (4, 4), facing up: F: Position (0, 3). facing down
G: Position (2, 1), facing left; H: Position (4, 4), facing left
Analysis: Initial state: The ant is at (0, 0) facing right.
Let's follow the ant's 's movement step by step:
Step 1: Ant is white cell (0, 0), facing right. It turns right, changes the cell to
black, moves forward to (1, 0). now facing down.
Step 2: Ant is on white cell (1, 0), facing down. It turns right, changes the cell to black, moves forward to (1, 4), now facing left.
Step 3: Ant is on white cell (1, 4), facing left. It turns right, changes the cell to black, moves forward to (0, 4), now facing up.
Step 4: Ant is on white cell (0, 4), facing up. It turns right, changes the cell to black, moves forward to (0, 0) now facing right.
Step 5: Ant is on black cell at (0, 0), facing right. turns left, changes the cell to white, moves forward to (4, 0), now facing up.
Step 6: Ant is on a white cell at (4, 0), facing up. It turns right, changes the cell to black, moves forward to (4, 1), now facing right.
Final state: The ant is at (4, 1) facing right. The answer is D.
Q3 (E):
After 12 steps, how many times did the cell position (0, 0) change its color? Answer with a number.
Analysis:
Initial state: The ant is at (0, 0) facing right. Target cell (0, 0) starts as white. Let's follow the ant's 's movement step by step:
Step I: Ant is on a white cell at (0, 0). facing right. It turns right, changes the cell to black, moves forward to (1, 0), now facing down. Target cell (0, 0) is visited and changes from white to black (change #1).
Step 2: Ant is white cell (1, 0), facing down. It turns right, changes the cell to black, moves forward to (1, 4), now facing left. Target cell (0, 0) remains black.
Step 3: Ant is on white cell (1, 4), facing left. It turns right, changes the cell black, moves forward to (0, 4), now facing up...
Step Ant on a white cell at (0, 4), facing up. turns right, changes the cell to black, moves forward to (0, 0), now facing right.... ...
Step Ant on a black cell at (0, 0), facing right. turns left, changes the cell to white, moves forward to (4, 0), now facing up. Target cell (0, 0) changes from black to white (change #2).
Step 6: Ant is white cell at (4, O), facing up. It turns right, changes the cell to black, moves forward to (4, 1). now facing right.
Step 7: Ant is white cell at (4, 1), facing right. It turns right, changes the cell to black, moves forward to (0, 1), now facing down.
... (Omitted: Step 8-11. Ant continues moving, flipping cells, but (0, 0) remains white.) Step 12: Ant is on a white cell at (0. 1), facing down. It turns right, changes the cell to black, moves forward (0, 0), now facing left. Target cell (0. 0) remains white.
Final state: The ant is at (0, 0) facing left. Target cell (0, 0) changed color 2 times. The answer is 2.
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J.3.4 WORD SEARCH
This game is a visual search task based on the classic Word Search puzzle paradigm. It features grid where each cell contains single letter. Target words are embedded within this grid, oriented horizontally, vertically, or diagonally (spanning eight possible directions).
Question types assess visual parsing and pattern recognition within the grid, including: (1) identi- fying the letter located at a specific row and column index; (2) counting the total occurrences of given letter across the entire grid; (3) determining the direction (out of eight possibilities) in which specified word extends, given its starting cell coordinates; and (4) locating both the starting cell coordinates and the correct direction a given target word within the grid. The complexity ('plot level') is influenced by the grid size.
Easy
Medium
Hard
5
5
7
7
8
1
2
3
4
5
2
3
4
6
7
2
3
4
5
6
7
8
1
O
Z
X
D
T
N
D
T
O
C
J
C
C
1
B
M
H
J
X
2
U
Z
U
V
H
G
2
Z
C
C
P
Z
A
F
3
S
G
P
K
X
R
W
3
A
X
T
N
4
O
Z
B
X
B
S
O
E
P
V
C
D
K
5
X
X
M
V
T
R
S
4
F
J
G
O
O
G
P
M
6
R
L
W
P
Q
R
P
N
J
5
W
O
Q
M
P
D
W
T
T
R
Y
O
S
K
C
X
J.3.5
2D TURING MACHINE
This game presents a simulation two-dimensional Turing machine. The state of the machine's tape, represented grid, is visualized using distinct colors for different symbols within each cell. The initial position of read/write head is indicated visually, typically by black dot, and also specified in the accompanying text description. The core task requires simulating the step-by-step operation of the defined Turing machine. Question types focus on tracking the machine's 's execution, including: (1) determining the head's coordinates after specified number of steps; (2) identifying the symbol (color) under the head after a specified number of steps; (3) describing the sequence of symbol changes within partieular cell over given number of steps; and (4) identifying the step number at which the machine first enters a specific state. The complexity ("Plot Level") " of the task primarily determined by the dimensions of the grid.
Easy
Medium
Hard
3
5
Muliie
J.3.6 TENTS
Tents is a logic puzzle played grid with predefined tree positions and row/column tent counts. The objective is to place tents adjacent to trees while following these rules: each cell holds either a tree, tent, remains empty; the number of tents matches the number of trees; every tent must be horizontally or vertically adjacent to at least one tree; no two tents can be adjacent in any direction (including diagonally); and row/column tent totals must match the given numbers.
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Questions involve analyzing partially filled grids, such determining the current number of tents in a row, remaining tents to place, identifying tree locations among given positions, available spots for new tents without immediate rule violations, and selecting rule-compliant tent placements. Puzzle difficulty scales with grid size.
Easy 7 x 7
Medium 10 x 10
Hard 13 x 13
2
2
3.
0-2
2 4
0.4
2.5
2.20
0
2.21
322
J.3.7 RHYTHM GAME
This is rhythm game featuring dynamic falling blocks. Players tasked with selecting column to place their finger and clicking on the operation blocks that fall to the first row of the selected column score points. Alternatively, players may choose not click any column, which will affect the falling of the blocks. The blocks in the game are divided into three types: Click blocks, Reverse blocks, and Snake blocks, each with different scores and click effects, prompting players to make choices while playing to get the highest score.
Questions will be asked based on the current game situation, involving issues such as block type identification, grid ratio calculation, and score calculation. Players need to reason and answer based on the information on the screen in the game. addition, the game difficulty divided into three levels according to the complexity of the scene, including Easy (150E4), Medium (150E6), and Hard (20CE6).
Easy
Medium
Hard 20 6
15 4
15 6
J.3.8 LIFEGAME
This is a cellular automaton simulation on an nCEn 2D grid, where cells are either "alive" (black squares) "dead" (white/empty squares) and evolve over generations. A cell's next state is de- termined by its current state and its eight neighbors: a dead cell with exactly three live neighbors becomes alive (simulating reproduction); alive cell dies with fewer than two (simulating under- population) or more than three live neighbors (simulating overpopulation), but survives with two or three.
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Game tasks involve counting current alive cells, predicting alive cells after one iteration, determining a specific cell's state change over N iterations, and calculating iterations for given region to reach stable state (static oscillating). The "Plot Level" (difficulty) is determined by the grid size, with larger grids indicating higher difficulty.
Easy
Medium 4 x 4
Hard x 5
3 3
0
2
0
2
3
J.3.9 MINESWEEPER
The game is inspired by Microsoft's classic game Minesweeper. The objective is reveal all cells that do not contain mines while correctly flagging the mines. If player accidentally reveals a cell containing a mine, the game ends immediately. The Minesweeper game board consists of cells marked with numbers (indicating the number of mines in the surrounding 3x3 grid), white revealed cells, gray hidden cells, flagged cells (marked with the letter "F"), and cells containing mines, which are unknown to the player. The game tasks include determining the status of cells, inferring the locations of mines, predicting the outcome of actions, and deciding on optimal reveal strategies. The difficulty levels are determined by the board size, with 4x4 being easy, 5x5 being medium, and 6x6 being hard. The board size and the number mines change based on the difficulty level.
Easy
Medium 5 5
Hard
4
x 6
Minesweeper Board
Minesweeper Board
Minesweeper Board
2
F
F
1
3
1
3
3
F
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J.4 STRATEGIC PLANNING
J.4.1 SOKOBAN
This game is based on the classic Sokoban puzzle game. The game scene consists of grid-based area featuring player (represented by a black humanoid figure), boxes (brown squares with X tex- ture), target points (green X marks), walls (brick-textured barriers), and movable areas (light brown floor). Players can move in four directions (up, down, left, right), push boxes forward, but cannot pull boxes or move through walls. The objective types evaluate spatial planning and logical reasoning: (1) predicting the player's final position after a sequence of moves; (2) predicting box's final position after a sequence of movements; (3) de- termining the minimum number of moves required to solve the puzzle; (4) identifying the current position of the player; (5) calculating the Manhattan distance between a box and its target point; and (6) finding the optimal sequence of moves reach specific position. The game difficulty is determined by the board size.
push all boxes onto target points. Question
Problem information
QA type
QA Level
Description
Q1
Target Perception
Easy Easy
Identify the current position of the player on the board Calculate the Manhattan distance between a box and its target
Q2 Target Perception
Q3
State Prediction
Medium
Given a sequence of player moves, predict the final position of the player Given a sequence of moves, predict the final position of the box
Q4
State Prediction
Medium
Q5 Strategy Optimization
Hard
Find the optimal sequence of moves to reach specific position
Q6
Strategy Optimization
Hard
Determine the minimum number of moves needed to solve the puzzle
Images and Plot Level division
Easy
Medium
Hard 12 x 12
5
5
8 8 8
0
2
3
4
X
2
3
M
H
Specific questions and analysis
Introduction: This is Sokoban puzzle where cartoon person is player, green X is target, brown box with is box to push, brown tiles are walls, and light brown areas are movable spaces.The coordinates (x, y) in this puzzle represent the matrix format.
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Q1 (M): What the current position of the player (row, column)?
Options:
[1] (6. 4) [2] (1, 4) [3] (3, 4) [4] (1.
[5] (4, 3) [6] (4, 1) [7] [8] (6.3)
Analysis: -laayer position: (1, 5) Boxes positions: (4, 3) Target positions: (5, 2) The player is
currently position (1, 5). So the answer is (I, 5). The option number 4.
Q2 (M):
What is the Manhattan distance between the box and the target?
Options:
[1] 15 [2] 16 [3] 6 [4]
[5] 12 [6] 1 [7] 14 [
Analysis: Player position: (1, 5) Boxes positions: (4, 3) Target positions: (5, Box position:
(4, 3) Target position: (5, 2) Manhattan distance = 14 51 + 13 21 = 2. So the answer is 2.
The option number is 4.
Q3 (E):
If the player makes these moves: Up Down Left Up Down Left Left Up, where will player end up? Options:
[1] (6. 5) [2] (1, 6) [3] (6, (4)
[5] (2, 6) 66] 2) [7] (1)
(2.))
Analysis: Player position: (1, 5) Boxes positions: (4, 3) Target positions: (5, 2) Move
sequence analysis: Initial position: (1 Move -Up: Failed Wall in the way (Player
stays at (1, 5)) Move 2 Down: Player moves from 5) to (2, 5) Move 3 Left: Player
moves from (2, 5) to (2, 4) Move 4 Up: Player moves from (2, 4) to (1, 4) Move
Down: Player moves from (1, 4) to (2, 4) Move 6 Left: Player moves from (2, 4) (2, 3) Move Left: Player moves from (2, 3) to (2, 2) Move 8 Up: Player moves from (2,
2) (1, 2) Final position: (1, 2). So the answer is (1, 2). The option number is 6.
Q4 (M):
Treat boxes as objects that can move by themselves, and treat people as floor (movable areas). After the moves up, right, down, up, left, right, up, left, where will the box that started at position (4, 3) end up?
Options:
[1] (2, 3) [ (3, 6) [3] (3, 1) [4] (1,5)
[5] (6, 2) [6] (4, 6) [7] (3, (6,
Analysis: Player position: (1,
Boxes positions: (4, 3) -arget positions: (5, Move
sequence: Move up: Box moved from (4, 3) to (3, 3) Move right: Box moved from (3, 3)
to (3, 4) Move down: Box moved from (3, 4) to (4, 4) Move up: Box moved from (4, 4)
to (3, 4) Move left: Box moved from (3, 4) to (3, 3) Move right: Box moved from (3, 3)
to (3, 4) Move up: Box moved from (3, to (2, Move left: Box moved from (2, 4) to
(2, 3) Box moves from (4. 3) to (2, 3). So the answer is (2, 3). The option number is 1.
Q5 (M):
Treat the boxes walls. What is the shortest sequence of moves for human move himself from position (1, 5) to position (1, 6)?
Options:
[1] Down [2] Left [3] Down Right Down [4] Right
[5] Right Down Left [6] Down Right Left
[7] Down Left Up [8] Left Down
Analysis: Player position: (1, 5) Boxes positions: (4, 3) Target positions: (5, 2) Start position:
(1, 5) End position: (1, 6) Optimal move sequence: Right. So the answer is Right. The
option number is 4.
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Q6 (M): What the minimum number of moves needed to solve this puzzle?
Options:
[1] 5 [2] 15 [3] 10 [4]
[5] 11 [6] 6 [8]
Analysis:
Player position: (1, Boxes positions: (4, 3) Target positions: (5, 2) Solution
analysis: Step-by-step solution: Player moves from (1, 5) (2, 5) Player moves from (2. 5) to (3, 5) Player moves from (3, 5) to (3, 4) Player moves from (3, 4) to (3, 3) Player moves from (3, 3) to (4, 3) (box moves from (4, 3) (5, 3)) Player moves from (4, 3) to (4, 4) Player moves from (4, 4) to (5, 4) Player moves from (5, 4) to (5, 3) (box moves
from (5, 3) to (5, 2) Total player moves: 8, So the answer The option number 8.
J.4.2 MAZE
This project focuses on generating question-and-ane -nddaweer datasets for a grid-based maze game. In this game, player, represented by red circle, must navigate path of white blocks to reach green goal block, while avoiding blue obstacle blocks. Movement restricted to the four cardinal directions. The generated questions are designed to evaluate range of cognitive abilities, primarily centered on spatial reasoning and pathfinding. These include tasks such as identifying the current locations of game elements, determining permissible moves, predicting the outcomes of specific actions, and deducing optimal routes to the goal. The complexity of the mazes and the associated questions scales, with mazes offered in Small, Medium, and Large sizes, and individual questions categorized by difficulty.
Images and Plot Level division
Easy
Medium 11 11
Hard 13 13
3
10
1
PJH
ECC
2
3
5
6
11
12
Problem information
QA type
QA Level
Description
Q1
Target Perception
Easy
Ask the position of player
Q2
Target Perception
Easy
Ask the position of goal within the maze
Q3
Target Perception
Easy
Ask the available directions to move are currently
Q4
State Prediction
Medium
The position after moving Find the path the goal
Q5
Strategy Optimization
Hard
Q6
Strategy Optimization
Hard
Count how many turs it takes to reach the finish
Specific questions and analysis
Introductio::
1. This is maze mini- game. The player needs navigate around obstacles to reach the destination and achieve victory.
2. The red circle represents the player, the green block is the goal and the blue blocks are obstacles.
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3. The player only move within the white blocks.
The coordinates are given in the format (row, col), where row represents the vertical position and col represents the horizontal position.
Q1 (E):
Which the following are the coordinates of the player?
Options:
A. (4, 6); B. (5, 5); C. (3, 5); D. (4, 4); E. (4, 5)
Analysis: Take look the game screen, the red circle represents the player. The coordinates of player are (4, 5), so the right option is E.
Q2 (E): Which of the following are the coordinates of the goal?
Optoins:
A. (7, 7); B. (7, 8); C. (6, 7): D. (7, 6); E. (8. 7)
Analysis: Take look the game screen, the green block represents the goal. The coordinates of goal are (7, 7), so the right option is A.
Q3 (E):
Which directions are available to move now?
Options:
A. up: B. down; up, down; D. up, right:
E. left, right; F. up, down, right;
G. down, left, right; H. up, down, left, right
Analysis: The player on (4, 5), and (3, 5) (5, 5) is empty. The player can move up, down.
Therefore, the option is C.
Q4 (E):
What are the coordinates of player after moving down?
Options:
A. (4, 6)
B. (5
C. (3,
D. (4, 4)
E. (4)
Analysis: Observe the screen, the position of player is (4,5), After moving down, the player (5. 5). Therefore, the right option is B.
Q5 (E):
Which sequence of movements will allow the player to reach the destination? Options:
A. left, left, left, right, right, left
B. down, right, right, down, down
C. down, down, left, up, right, down
D. up. up, up, right, up, down
E. left, down, left, right, down, left
Analysis:
Lets figure out the path to the goal step by step: Step 1. Go down, from (4, 5) to (5, 5). Step 2. Go right, from (5, 5) to (5, 6). Step 3. Go right, from (5, 6) (5, 7). Step 4. Go down, from (5, 7) to (6, 7). Step 5. Go down, from (6, 7) to (7, 7). Achieved the goal! Therefore, the right sequence of movements are: down, right, right, down, down. The right option is B.
Q6 (E):
Find the path to the finish and count the number of turns it takes get there. Provide one number.
Analysis:
First, lets figure out the path to the goal step by step: Step 1. Go down, from (4, 5) to (5,
5). Step 2. Go right, from (5, 5) to (5, 6). Step 3. Go right, from (5, 6) to (5, 7). Step 4.
Go down, from (5, 7) to (6. 7). Step 5. Go down, from (6, 7) to (7, 7). Achieved the goal! Therefore, the path is: (4, 5), (5, 5), (5, 6), (5, 7), (6, 7). (7, 7). Then, let's count the number of turns step by step: Step 2. Turn detected: from down to right. Step No turn detected. Step Turn detected: from right down. Step 5. No turn detected.
In summary, the total number of turns
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J.4.3 TICTACToE
This game is derived from the classic Tic-Tac-Toe game, featuring 30E grid with two players represented by red and blue grid markers respectively. The objective is to create a straight line of three same-colored markers either horizontally, vertically, diagonally win. Question types include: (I) determining the color of specific grid cells, (2) identifying the optimal move for the current player, and (3) predicting the opponent's 's best response after given move. The difficulty scales across three levels based on scenario complexity, where higher difficulty requires evaluating progressively more decision-maie makinng conditions answer the same question types, systematically testing the model's 's strategic reasoning and conditional judgment capabilities.
Images and Plot Level division
Easy
Medium
Hard
2
2
2
0
1
1
9
2
2
2
M
Question information
QA type
QA Level
Description
Target Perception
Easy
Questions about the current state of a specific block of the board.
Q2 Strategy Optimization
Medium
Questions about the optimal strategy to take a move of the current player of the board.
Q3 Strategy Optimization
Hard
Questions about the outcome to take a specific move of the current player of the board, and the optimal strategy to take move of the opponent player after the specific move.
Specific questions and analysis
Introduction:
Tic-Tac- To-TTccTTe is classic two-player game played on 3x3 grid, (row, col) from (0, 0) to (2, 2). Players take turns marking space in the grid, one using ***** (the red block) and the other using ***** (the blue block). In each game, player ***** starts first. The objective is to be the first to get three your marks in a row (horizontalll,, vertically, or diagonally). If all nine squares are filled without either player achieving this, the game ends in draw. Notice: the current player to make a move should be inferred from the number of pieces for each players on the board. When inferring the optimal move, if optimal move be inferred some rules, choose the optimal move. Otherwise, choose the first move. (The order of choices is (0, 0), (0, 1), (0, 2), (1, 0), (2, 2), choose the first move that is not occupied)
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Q1 (E): Question: What is the color of the block (0, 0)?
Options: A. red; B. blue; C. white
Analysis: The current board is [["O', "O', 'X', ['X", 'X', 'O'], '0''""'' '0", 'X']]. The block at (0, 0)
is "O", and the color matching "O" red, the block (0, 0) is red. The answer is A.
Q2 (M): What is the optimal move for the current player? no move exists, choose the answer
"None".
Options: None; B. (0, O); C. (0. I); D. (0, 2): E. (1, O); E. (L. D); (1, 2); H. (2, 0) or (2, 1) or (2, 2)
Analysis:
The current board is [[' 'O', 'I. "",
'X', 'O']]. Since the player "O"
plays first in each game, if the count of "O" is the same as "X", the current player is "O". Otherwise, the current player is "X". The count of "O" is and the count of "X" is 1, so the player now is X. Current player is X. opponent is O. Must block opponent O's
potential double threat on Row and Top-left bottom-right diagonal, so player X should choose position (0, 0). The answer is B.
Q3 (H):
If the current player moves (0, 2), will this move be successful? If not, choose the answer "None". If successful, will the current player win immediately? If yes, choose the answer "None". Otherwise, what the opponent's optimal move following this step?
Options: A. None; B. (0, O); C. (0, I); D. (0, 2); E. (I, 0); F. (1, 1); G. (1, 2): H. (2, 0) or (2, 1) or (2,
Analysis:
Yes, this move will be successful. The current board is [['' " [':
'X', 'O'], (",,
"J]. Since the player "O" plays first in each game, if the count of "O" is the same
"X", the current player is "O". Otherwise, the current player is "X". The count of "O"
and the count of "X" is so the player now is O. Since the current player O moves to (0, 2), the current player won't win immediately, After that, current player is X, opponent is O. Must block opponent O's winning threat on Column 2, player X should choose position (2, 2). The answer H.
J.4.4 ULTRA TICTACTOE
Ultra TieTacTToe is an advanced variant of TicTac'To played on a 3x3 grid of 3x3 subgrids (Nine- grids). Players alternate placing "X" (first player) and "O" (second player) markers using four- coordinate system ri.j..o,cool), where (i.j) denotes the subgrid position and (row,col) specifies the cell within that subgrid. The initial move must be made the central Nine-grid (2,2), with sub- sequent moves constrained to the subgrid determined by the opponent's 's previous move position. Scoring occurs when three identical markers form a line within any subgrid (each such line counts as I point). The game concludes when all nine central cells of the subgrids are occupied. Ques- tion types involve analyzing board states (identifying marker ownership at coordinates), calculating available move options, quantifying marked cells, evaluating scoring patterns within subgrids, and determining optimal strategic placements. Game complexity tiers are defined by move count ranges: Easy (10-34 steps), Medium (35-59 steps), and Hard (60-81 steps).
Easy
Medium 35-59 steps
Hard 60-81 steps
10-34 steps
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J.4.5 SPACE INVADERS
Adapted from the classic arcade game, Space Invaders is simplified space warfare game. Players control ship at a grid's bottom, moving it by column to fire lasers upward. Lasers destroy the nearest alien invader in that column (different colors worth 10, 20, or 30 points), earning points and potentially exposing others. Collectively moving enemies add dynamic challenge. The game uses visually intuitive images for ship and aliens instead of text symbols.
Players analyze game scene images to answer questions covering: game state perception (e.g., en- emy counts by location color); single-shot outcome prediction (points from current post-move shots); effects of consecutive shots in dynamic scenarios; and strategic planning for maximum points. These questions range from simple recognition complex reasoning. Three difficulty levels based on scene complexity; higher levels feature larger grids with more numerous and complexly arranged enemies, demanding greater player skill.
Easy
Medium
Hard
3
5
6
7
10
3
4
5
6
+
7
2
J.4.6 SNAKE
This game is derived from the classic game Snake, which involves a square white grid scene with coordinates, with snakes and food represented by colored squares. The snake head is represented by yellow square, the snake body by blue squares, and the food by a red square. Each step the snake can move in four directions: up and down, left and right. The game ends if the snake head hits the bound of the grid or own body.
The questions include 1.
The coordinate of the snake head. 2. The coordinate of the food. 3. The length the snake. 4. Which will happen until this process ends following a specific sequence of moves (hitting its own body, hitting the wall, reaching the food, nothing happens)? 5, The length of the shortest path to reach the food. Plot Level determined by the grid size.
Easy
Medium
Hard
5 5
10 x 10
15 x 15
J.4.7 CHESS RANGER
Chess ranger is derived from chess. The game presents a problem with an SEE8 chessboard image containing 6 pieces, where the possible types of pieces are King, Queen, Rook, Bishop, Knight, and
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Pawn. The goal of the game is to use the movement and capture rules of chess pieces to ensure that only one piece remains on the board at the end.
The types of questions in the game are follows:1.The number of pieces of certain type the board.2.The identity of the piece located in a specific square on the board.3. The location particular type of piece the board.4.The required number of steps to solve the current chessboard configuration.5.. The moves that can solve the puzzle among several possible options. The difficulty level of the game determined by the number of pieces the board: 4, pieces corresponding to easy, medium and hard.
Easy
Medium
Hard
4 pieces
5 pieces
6 pieces
J.4.8 PACMAN
The game inspired by the classic maze game Pac-Man, with the original four ghosts simplified to just two ghosts. The objective of the game is for Pac-Man to eat as many beans as possible while avoiding being caught by the ghosts. The game scene includes Pac-Man, beans, walls, and ghosts (Pinky and Blinky), with Pac-Man, Pinky, and Blinky represented by special images. The beans are represented small yellow circles, and the walls are dark blue squares. Pac-Man, Pinky, and Blinky cannot move through walls. The dataset includes tasks such as determining Pac-Man's 's current position and direction, counting the number of beans in specific area, predicting the paths of the ghosts, forecasting the outcome of Pac-Mac Man's 's movements, and analyzing strategies to maximize the score while avoiding ghosts. The dataset is divided into three difficulty levels based on grid size: Easy (16x16), Medium (18x and Hard (20x20).
Easy
Medium
Hard
16 16
18 18
18 18
13
67


Reviews:
[{"review_id": "ZtNupJJLMX", "reviewer": "Reviewer_grko", "summary": "This paper introduces Game-RL, a framework for vision-language reinforcement learning that uses video game environments as multimodal, verifiable training data. Using the proposed Code2Logic pipeline, game code is transformed into reasoning-oriented visual question–answering (VQA) tasks, producing the GameQA.", "questions": "- What proportion of games required manual correction or external code reuse?\n- What is the importance of task templates? \n- What happens if you just SFT on GameQA?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Scalable data-generation pipeline: Code2Logic programmatically maps game code to reasoning logic and auto-generates verifiable multimodal QA data.\n- Demonstrates that purely synthetic, self-verifiable environments can modestly improve general VLM reasoning—important for RL reproducibility.\n- Diverse benchmark coverage (3D perception, pattern matching, planning, reasoning).\n- Clear, reproducible methodology; good visualizations and qualitative examples.", "weaknesses": "- Small gains on external benchmarks are statistically and practically modest; no significance tests or efficiency comparisons.\n- Lack of ablation isolating contributions of Code2Logic data vs RL itself (no SFT vs RL comparison on the same data).\n- Evaluator bias: rewards rely on QwQ-32B, potentially aligning to its own style and inflating self-consistency.\n- Unclear verification metrics: “verifiable” is claimed, but no automated correctness guarantees are quantified.", "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": "W6s2GH0f1r", "reviewer": "Reviewer_gdbT", "summary": "This paper addresses the challenge of improving the general reasoning capabilities of VLMs, arguing that current RL training is overly focused on narrow domains like geometry and charts. The authors propose Game-RL, a framework that leverages video games as a rich, verifiable, and scalable source of training tasks. They introduce Code2Logic, a novel pipeline that uses LLMs to synthesize a large-scale dataset, GameQA, by first generating game code and then using that code to create verifiable VQA tasks. The central finding is that training VLMs on GameQA using GRPO with a simple outcome-based reward signal unexpectedly improves performance not only on unseen games but also across a suite of 7 diverse, out-of-domain general reasoning benchmarks.", "questions": "* In Appendix B.3, you note that training on a subset of 4 games led to better generalization than training on 10 games. You speculate this is due to \"random factors\" or a \"well-chosen set.\" Could you elaborate on this?\n\n* The Code2Logic pipeline works well for the 30 games presented, which are largely deterministic, static, or turn-based. How do you see this framework extending to more complex, real-time games with non-deterministic physics (e.g., Angry Birds) or continuous action spaces (e.g., a racing game)? Does the reliance on LLMs to generate the game code fundamentally limit the complexity of the games that can be synthesized?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* This paper is clear writing and easy to follow.\n\n* Dataset contribution: GameQA spans 30 games / 158 tasks with explicit difficulty control and verifiable answers.\n\n* GRPO on GameQA yields consistent improvements on diverse general benchmarks", "weaknesses": "* The Code2Logic pipeline is presented as highly scalable, but Section 2.4 and Appendix F.4 reveal a significant reliance on manual verification at every step (code, data engine, and augmented samples). Furthermore, the data augmentation relies on paraphrasing from InternVL2.5-78B, and data quality checks use commercial LLMs. This \"human-in-the-loop\" and \"proprietary-LLM-in-the-loop\" requirement makes the process less automated and scalable than implied.\n\n* The work only generates massive amounts of game QA data; I don't believe this is the primary task that a 'game agentic' model should be excelling at. Instead, the model should be interacting directly within the code-driven game to generate trajectories for SFT or RFT, or to perform RLVR.", "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": "4BQTH6Iwh6", "reviewer": "Reviewer_amrf", "summary": "This paper addresses the narrow domain focus of current reinforcement learning (RL) methods for Vision-Language Models (VLMs), arguing that such limitations hinder the development of general reasoning. To this end, the authors propose Game-RL, an approach that leverages diverse and verifiable video game environments for training. To generate the necessary training data, they introduce Code2Logic, an LLM-assisted, three-stage pipeline for synthesizing game code, task templates, and ultimately, game-based VQA samples. The primary tangible contribution is the resulting GameQA dataset, a large-scale collection of 140K question-answer pairs spanning 30 distinct games and 158 tasks, categorized by cognitive skills. The authors demonstrate that training VLMs solely on GameQA via an RL algorithm (GRPO) yields consistent performance improvements across a suite of seven external, general-purpose vision-language benchmarks, thereby providing evidence for the transferability of reasoning skills learned within these game environments.", "questions": "1. The paper highlights the Code2Logic pipeline as a key contribution for generating the GameQA dataset. However, Appendix E.3 notes an average of 7.5 hours of human effort was required per game. This suggests a significant reliance on manual oversight. Therefore, \na) What are the most common failure modes of the LLMs in this pipeline? What kinds of errors (e.g., logical inconsistencies in game rules, un-compilable code, nonsensical QA pairs) required the most frequent manual intervention?\nb) Given this reliance on expert human-in-the-loop validation, how do you envision this approach scaling to a much larger and more diverse set of games (e.g., hundreds or thousands) without becoming prohibitively expensive and time-consuming?\n2. Justification of the Reward Design. The RL training utilizes a sparse, binary reward based on the final answer's correctness. However, your GameQA dataset commendably contains detailed, step-by-step reasoning chains in the \"analysis\" field for each question. This rich information seems ideal for process-based supervision or a denser reward signal that could more effectively guide the model toward correct reasoning patterns.\na) Did you experiment with using these ground-truth reasoning steps to shape the reward signal (e.g., via sequence matching or a separate reward model trained on the analysis text)?\nb) If not, could you elaborate on the rationale for choosing a sparse, outcome-based reward? Especially for complex reasoning, why is this preferable to process supervision, which is often considered more effective for teaching reasoning?\n3. Reliability of the LLM-as-a-Judge. The entire Game-RL training loop hinges on the accuracy of the Qwen2.5-72B model acting as the judge for reward allocation. The validity of your experimental results is therefore contingent on the reliability of this judge.\nCould you please provide an evaluation of the judge model's performance on the GameQA test set? Specifically, what are its accuracy, precision, and recall when compared against the ground-truth answers? An agreement score against human evaluators on a representative sample would be particularly insightful.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The motivation is clear and sound. The authors correctly identify a critical limitation in the current VLM training paradigm, the over-reliance on narrow, static domains like geometry or chart reasoning. The proposal to use video games as a more dynamic, verifiable, and diverse training environment for fostering general reasoning is an interesting idea. \n- The GameQA dataset could be a substantial contribution to the community. Its scale (30 games, 158 tasks, 140K samples), diversity across four distinct cognitive categories, and controllable difficulty levels make it a valuable resource for both training and benchmarking VLMs. The effort invested in its creation is evident, and it provides a tangible asset that can spur further research in this area.\n- The paper presents a comprehensive and rigorous experimental evaluation. The authors test their approach not just on a few models but on a range of popular open-source VLMs. Crucially, evaluation is not confined to their own dataset; they demonstrate generalization by testing on seven diverse external benchmarks. The inclusion of ablations on data quantity and game diversity further strengthens the quality and credibility of the empirical findings.\n- The paper is well-written, logically structured, and easy to follow. The Code2Logic pipeline is explained with a clear diagram (Figure 1), and the examples provided throughout the paper and appendix effectively illustrate the nature of the tasks in GameQA. This clarity makes the work accessible and its contributions understandable.", "weaknesses": "1. The novelty is somewhat incremental and limited. The Code2Logic pipeline, while well-executed, is fundamentally a specific instance of LLM-based synthetic data generation. This approach is becoming increasingly common, and the paper does not sufficiently differentiate its technical contribution from existing work in program-aided or LLM-driven data synthesis. It seems to be more of an extensive engineering protocol than a novel, generalizable method. Similarly, Game-RL is an application of an existing RL algorithm (GRPO) to a new dataset. The name might imply a novel RL framework tailored for games, which is not the case. The contribution is thus more of an empirical finding (\"RL on game data generalizes\") rather than a new RL technique.\n2. There is a lack of reproducibility in data generation. The Code2Logic pipeline is described as \"LLM-assisted,\" but the degree of human intervention appears substantial, undermining its scalability and reproducibility. The appendix notes an average of 7.5 hours of human effort per game. This suggests a highly iterative and manual process of prompt engineering, code verification, and template refinement. For the work to be truly reproducible, the authors should provide a much more detailed account of this human-in-the-loop process, including the specific prompts, failure cases encountered with the LLM, and the nature of the manual corrections required.\nInsufficient Justification for the RL Setup: The design of the reinforcement learning component is not adequately justified and appears suboptimal for the stated goal of improving reasoning.\n3. The use of a sparse, binary (0/1) reward based only on the final answer is a weak signal for complex, multi-step reasoning tasks. It provides no credit for partially correct reasoning chains and fails to guide the model on how to arrive at the correct answer. The analysis field already present in the GameQA dataset seems perfectly suited for developing a more informative, process-based reward signal. The paper would be significantly stronger if it explored this or at least provided a detailed justification for why this simpler reward scheme was chosen.\n4. The use of a powerful VLM (Qwen2.5-72B) as a judge introduces a potential ceiling effect and a source of bias. The paper provides no evaluation of this judge's accuracy on the GameQA tasks. Without knowing the reliability of the reward model, it is difficult to assess the quality of the training signal the agent receives. The authors should report the judge's agreement rate with human annotations on a sample of the data.\n5. While the paper claims to boost \"general reasoning,\" the connection is supported by relatively small performance gains on the external benchmarks. Furthermore, the selection of the 30 games appears somewhat arbitrary. The claim would be more convincing if the game selection process can demonstrate diversity and wide coverage.", "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": "VOgpiNU6XN", "reviewer": "Reviewer_hgzh", "summary": "The paper proposes a method for verifiable synthetic vision-language data generation with video games, by training VLMs on this synthetic data with GRPO, they get improvements in other benchmarks for vision language models.", "questions": "1. How does performance change on sequential decision making tasks requiring vision? Perhaps on some of the game benchmarks you mentioned? It feels weird mentioning other game environments and then not testing performance on them.\n\n2. Would it be possible to test more seeds to get error bars? Are the improvements significant or within error range? I understand that these experiments are likely quite expensive, but at least 3 seeds on some of the trainings and tasks could be useful, especially given the tight margin of improvement.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The verifiable synthetic data generation is a timely and important problem given the landscape of LLM/VLM trainings with RL.\n2. Using games to improve vision capabilities in a verifiable way is an interesting contribution.", "weaknesses": "1. The games used for data generation are all grid-like games with very simple interfaces.\n2. Games are inherently sequential-decision making tasks, so it feels strange that the authors did not then test performance on any of the game benchmark they mention. This seems like a natural extension to the project so far.\n3. The performance improvements of 1 to 2% are quite small, and using a single seed might not be enough to properly test this.", "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": "DVzzyGBYcq", "reviewer": "Reviewer_D6PT", "summary": "To address the lack of large-scale, high-quality data for RL training in video game contexts, this work:\n(1) Proposes *Code2Logic*, a pipeline that leverages LLMs to synthesize reasoning data based on game code; and\n(2) Constructs *GameQA* a large dataset of diverse tasks and 140K VQA examples. After post-training MLLMs on this dataset, the authors report improved reasoning performance across multiple general-domain benchmarks, achieving better out-of-domain generalization compared to other comparable datasets.", "questions": "•\tAs mentioned in Section 2.2, the QA templates are either (1) human-designed and refined by LLMs, or (2) generated by LLMs and manually validated by humans. What is the proportion between these two approaches, and what are the respective time costs involved in each process?\n•\tThe *Code2Logic* pipeline employs LLMs in multiple stages while keeping humans in the loop. As I understand it, this process still requires considerable manual effort. Have the authors considered adopting a more agentic approach to further reduce human labor by leveraging LLM agents more extensively?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "•\tData Quality: A wide range of video games are selected, ensuring diversity and strong generalization potential in the training data. The synthesis process leverages LLMs across multiple stages and includes manual validation, which guarantees both the efficiency and reliability of the data generation pipeline.\n•\tClarity: The approach is clearly presented, supported by a well-illustrated pipeline diagram and numerous examples in the appendix. The experiments are reported in a detailed and organized manner. \n•\tSignificance: The experiments cover a diverse set of models and benchmarks, outperforming most comparable datasets. Comprehensive ablation studies, such as scaling effect analyses, further strengthen the empirical evidence. Together, these results provide strong support for the overall effectiveness of GameQA.", "weaknesses": "•\tGame-RL can to construct large-scale datasets for diverse games at relatively low cost. Nevertheless, the idea of synthesizing game-based benchmarks to enhance MLLMs’ reasoning ability via RL is a well-established and commonly explored direction. There already exist many high-quality, large-scale benchmarks—such as VisualSphinx (https://arxiv.org/abs/2505.23977) VGRPBench (https://arxiv.org/abs/2503.23064) and other recent game-based reasoning datasets—that share similar objectives, and some of them also asserts that RL training on their dataset can improve MLLMs’ reasoning ability in general-domain. Therefore, the overall objective of this paper is not highly novel.", "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)