Can Large Language Models Master Complex Card Games?
Authors: Wei Wang
Fuqing Bie
Junzhe Chen
Dan Zhang
Shiyu Huang
Evgeny Kharlamov
Jie Tang
Venue: NeurIPS 2025 poster ()
Keywords: Language Model
Evaluation
Primary area: evaluation

Abstract: Complex games have long been an important benchmark for testing the progress of artificial intelligence algorithms. AlphaGo, AlphaZero, and MuZero have defeated top human players in Go and Chess, garnering widespread societal attention towards artificial intelligence. Concurrently, large language models (LLMs) have exhibited remarkable capabilities across various tasks, raising the question of whether LLMs can achieve similar success in complex games. In this paper, we explore the potential of LLMs in mastering complex card games. We systematically assess the learning capabilities of LLMs across eight diverse card games, evaluating the impact of fine-tuning on high-quality gameplay data, and examining the models' ability to retain general capabilities while mastering these games. Our findings indicate that: (1) LLMs can approach the performance of strong game AIs through supervised fine-tuning on high-quality data, (2) LLMs can achieve a certain level of proficiency in multiple complex card games simultaneously, with performance augmentation for games with similar rules and conflicts for dissimilar ones, and (3) LLMs experience a decline in general capabilities when mastering complex games, but this decline can be mitigated by integrating a certain amount of general instruction data. The evaluation results demonstrate strong learning ability and versatility of LLMs. The code is available at 
https://github.com/THUDM/LLM4CardGame

Can Large Language Models Master Complex Card Games?
Wei Wang 12, Fuging Bie², Junzhe Chen², Dan Zhang?
Shiyu Huang" Evgeny Kharlamov?, Jie Tang"*
Nankai University, Tsinghua University, 'Beijing University of Posts and Telecommunications Zhipu AI, Bosch Center for Artificial Intelligence
weiwangorg@163.com
Abstract
Complex games have long been an important benchmark for testing the progress artificial intelligence algorithms. AlphaGo, AlphaZero, and MuZero have defeated top human players in Go and Chess, garnering widespread societal attention towards artificial intelligence. Concurrently, large language models (LLMs) have exhibited remarkable capabilities across various tasks, raising the question of whether LLMs achieve similar success in complex games. In this paper, explore the potential of L.LMs mastering complex card games. We systematically assess the learning capabilities of LLMs across eight diverse card games, evaluating the impact of fine-tuning on high-quality gameplay data, and examining the models' ability retain general capabilities while mastering these games. Our findings indicate that: (1) LLMS can approach the performance of strong game AIs through supervised fine-tuning on high-quality data, (2) LLMs can achieve certain level of proficiency in multiple complex card games simultaneously. with performance augmentation for games with similar rules and conflicts for dissimilar ones, and (3) LLMs experience a decline in general capabilities when mastering complex games, but this decline can mitigated by integrating a certain amount of general instruction data. The evaluation results demonstrate strong learning ability and versatility of LLMSs. The code is available btttps://gthin com com/THUDM/ LLM4CardCame
1 Introduction
long-term goal of artificial intelligence is to achieve superhuman performance in highly challenging domains [T33.. Games, particularly complex ones such as Chess and Go, have become the best testing grounds for artificial intelligence algorithms (477) In recent years, artificial intelligence algorithms have made significant breakthroughs in the realm of games. AlphaGo is the first to defeat human professional players in Go by using supervised learning from expert human data and reinforcement learning (4) Following this, general reinforcement learning algorithm, AlphaZero, achieves superhuman performance in three challenging games: Chess, Shogi (Japanese chess), and Go (6) MuZero even achieves performance equivalent to AlphaZero without needing know the rules of the game (7].
Recently, large language models (LLMs) [8-11 have achieved remarkable performance, surpassing human levels, across a wide range of tasks including general knowledge question answer- ing 12 [3,, mathematics [4 15). coding [16 17), and agent [18 19. This naturally raises the question: can language models achieve superhuman performance in complex games, or at least reach the same level as the best reinforcement learning algorithms? In this paper, focus card games.
CCoresponning author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Currently, there is a vast amount of research evaluating LLMs across various tasks. Among these studies, some research also evaluates the decision docision-making capability of LLMs in both board games and card games, such as Texas Hold" fooddeem em [20 21,, Blackjack (22), and Guandan [23 [4] However, these studies still have some limitations. First, many evaluation studies assess L.MMs through prompting without involving fine-tuning [25] 26] These prompt-based evaluation studies can only assess whether LLMs are capable of applying their existing knowledge to new environments. But, they do not evaluate the learning ability of LLMs. Second, some evaluation studies include assessments of LLMs after fine-tuning and demonstrate that fine-tuning improves the performance of LLMs in new environments. But, the tasks evaluated in these studies lack sufficient complexity, making them inadequate to comprehensively assess the learning capabilities of LLMs.
As mentioned at the beginning, complex games are often used to explore the upper bound of artificial intelligence algorithms' learning capa- bilities. Therefore, this paper, we investigate whether language models can master complex card games. To address the shortcomings of previous work, we systematically evaluate the
Complexity of Different Games
10
engieyy Impurtic Inferuaiion
Rtichi Mahjong
ARy Infosee Size
10"
Guannaa
Actas Sti
performance of language models on eight care- fully selected card games. First, most games exhibit a high level of complexity as shown in Figure The high complexity of these games presents greater challenge to the learning abil- ities of large models. Evaluations across the eight games provide a more comprehensive un- derstanding of LLMs. Second, we evaluate the performance ceiling that LLMs can achieve by fine-tuning the model on high-quality gameplay interaction data. Compared to prompt- based methods, fine-tuning methods focus on evaluat- ing the learning ability of language models. We generate high-quality gameplay interaction data using strong game AIs or directly utilize publicly available high-quality interaction data. Specifically. we focus on the following three research questions:
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Uho
Livit Trise Hald
No-Brii Toox Uoiid
Hollen
Choss
10 Ig
10
InfoSet Number
Figure 1: Complexity of the games. InfoSet Num- ber: the number of the information sets; Avg. In- foSet Size: the average number of states in single information set.
1. Can LLMS master complex card games? And how much data is required to master these games?
Can LLMs simultaneously master multiple games? Do different games mutually enhance each other or do conflicts arise between them?
Can LLMs maintain their general capabilities while mastering complex games?
To answer these questions, we first fine-tune language models on each of the eight games separately to evaluate the extent to which the models can master individual games. Next, fine-tune the models on a mixture of all the game data to assess their ability to master all the games simultaneously. Finally, evaluate whether the models' general capabilities decline using MMLU-Pro [12]. Math-500 (27). and HumanEval (28) benchmarks for knowledge question answering, math, and coding skills. Addi- tionally, we analyze the performance variations of language models with different parameter sizes and types (Qwen2.5 29 Llama3.1 330, and GLM4 (9). In summary, the contributions of this work are:
We are the first to propose a comprehensive evaluation of the learning capabilities of LLMs across multiple high-complexity games, which present greater challenges to the learning abilities of LLMs.
We obtain a large amount of high-quality data for LLMs to learn by utilizing strong game AIs and game prompt templates, avoiding the problem of high computational resource consumption when LLMs learn from scratch in the environment.
We thoroughly assess the learning ability of the models through experiments in three aspects. of L.LMs, as they
The evaluation results demonstrate strong learning ability and versatility can simultancously master multiple complex games without altering the model structure.
2

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2
Related Works
The integration of LLM--asedd agents into card and board game has garnered significant attention, focusing on enhancing strategic reasoning and adaptability [1+333 Previous research has mainly focused improving LL-M-based agents in card games with incomplete information and adversarial
settings, such as Texas Hold" em [20 [3]]. Blackjack [22], and Guandan [23) 24 For example, Yim et al. (23] introduced a Theory of Mind (ToM) planning technique for Guandan, enabling LLM agents to adapt strategies based on game rules, state, and history. Their approach also integrates an external tool manage the game's s large action space. While effective in a single game, the generalizability of such coompt-baseds strategies across multiple games remains an open question. prompt- paset-basedddd- approach that leverages GPT-4's reasoning
Guo et al. [2]] proposed Suspicion-Agenn and high-order ToM capabilities to adapt strategies in imperfect information card games. It enables GPT-4 to play against different opponents using only game rules and observations as input. Their experiments demonstrate its effectiveness across multiple games. However, as prompt-based method, relies solely on the model's inherent knowledge, which limits its overall performance.
3 Method
From AlphaGo, to AlphaZero, and then to MuZero, we can see that these methods have achieved significant breakthroughs in complex games by continuously exploring the environment and leverag- ing successful experiences. This paper aims to explore whether LLMs can master complex games similarly. Considering the time and resource consumption involved in the exploration process, we utilize existing strong game AIs to generate high-quality trajectory data. This study investigates whether LLMs can master complex games by learning from this high-quality trajectory data. Next, we introduce the selected games and the process of generating training data for each game.
3.1 Games
For game selection, we primarily consider the popularity, complexity, and availability of high-quality models or data. Based on these three aspects, we select eight games: DouDizhu, Guandan, Riich Mahjong, Uno, Gin Rummy, Leduc Hold' em, Limit Texas Hold' em, and No-Limit Texas Hold ee..
DouDiZhu. DouDizhu (a.k.a. Fighting the Landlord) is the most popular card game in China. The game is played by three players with standard 54-card deck. There are two roles in the game: a landlord and two farmers. Some studies have explored building strong DouDizhu Als using techniques such as handcrafted heuristic rules, reinforcement learning based methods, and search algorithms (75 Among these methods, DouZero (7 is simple and effective approach that requires no human knowledge or state/action abstraction. It is currently the strongest publicly accessible DouDizhu AI.
GuanDan. Guandar is another popular card game in China. The game requires four players, with the two players sitting opposite each other forming a team. Its gameplay is similar to Dou Dizhu, However, Guandan is more complex in comparison. There is relatively less research on Guandan AI 38 39). Among the existing work, DanZero (38), which employs neural network-enhanced Monte-Carlo method, has outperformed other algorithms. Therefore, we choose DanZero as our teacher model.
Mahjong. Mahjong is widely popular multiplayer tile-based game across the world. Mahjong has many variants, and this paper focuses on Richi Mahjong (a.k.a. Japanese Mahjong). Suphx is the first Mahjong AI to defeat most top human players [40). Then LuckJ developed by Tencent reached 10 Dan on Tenhou and surpasses all human players and other AIs. However, the model weights for both of these AIs have been made publicly available. Nevertheless, Tenhou provides gameplay data from expert players.
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Game
Players
Teans
Teacher modeV/huaa
Oppnnent
Steps
Avg.
Player
Steps (Pilteeed
Aug. Legal Actions
Training data
per
Stop
DoaDicha
Rule
37.31
2050kk
10.06
1,000K
GuanDtan
311.25
1.220k
1,000k
Ricci Mahjong
experts
656.92
1,900
1.000L
42.33
410k
400K
Gin Rummvy cas
Rule
Random
52.14
1
400K
DON medel
Random
3.61
SHUK
400K
Limi Texas Hold'ng cm No limit Posas Hold cm
DON model
Random
2006
5.01
2.50
450k
2.96
400k
DQN model
Random
4006
3.78
1.89
700K
4.31
400K
Table 1: Data generation information of games.
Uno. Und is a proprietary American shedding-type ttapiinnngteeeeeeeee card game. The game is played with a specially designed 108-card deck. There are 2 players in the game. Each player starts with seven cards dealt face down. Players take turns matching the card in the Discard Pile by number, color, or symbol/action. The objective is to be the first player get rid of all the cards in hand.
Gin Rummy, Gin Rummy is a two-player card game. The game is played by two players using a standard 52-card deck. The dealer deals 11 cards the opponent and 10 cards himself. During each turn, you can pick up the discard or draw from the face-down stock, then discard a card. Players try to form melds of or more cards of the same rank or 3 or more cards of the same suit in sequence.
Ledue Hold'en em. Ledue Hold" em, introduced in Southey et al. [41] is simplified variant of Limit Texas Hold' em. om. This version uses a deck comprising only six cards, with two pairs each of King, Queen, and Jack. The game involves two players, spans two rounds, and has a maximum of two bets per round, with the raise amounts fixed at in the first round and in the second round.
Limit Texas Hold'em. Limit Texas Hold' en is a well-known betting game with 52-card deck. Each player receives two face-down cards, known as hole cards. Subsequently, five community cards are dealt in three stages: the flop, the turn, and the river. Players aim to form the best possible five-card hand using any combination of their hole cards and the community cards.
No-limit Texas Hold'ems No-limit Texas Hold' em follows similar rules to Limit Texas Hold' em but with key differences in betting. No-limit Texas Hold' em allows players to raise by at least the amount of the previous raise in the same round and up to the entirety of their remaining stack. Additionally, there is no limit on the number of raises in No-limit Texas Hold's em.
3.2 Data Preparation
Trajectory Generation. We generate gameplay interaction data by having the teacher model compete against opponents. The teacher model and opponent information for each game shown in Table For DouDizhu, we use DouZero (37) as the teacher model and a rule-based model [42] the opponent model. For GuanDan, we use DanZero (38) as the teacher model and rule- based 138 model as the opponent model. For Riichi Mahjong, we download the match data of human professional players from the Tenhou" Morta a strong Mahjong AI, which is used only during evaluation. For Uno and Gin Rummy, we use rule model from Zha al. [2] the teacher model and use random as the opponent. For Ledue Hold' em, Limit Texas Hold' lo,d'em, No-limit Texas Hold' em, we train DQN model as the teacher model with RLCard framework
for the year 2020. The opponent model is
Based on the complexity of different games, we play each game a varying number of times. The important information of the generated data is shown in Table From the table, it can be seen that the average number of steps in the games Doudizhu, Guandan, and Mahjong is significantly higher than the other games. Particularly, Guandan and Mahjong have longer steps because each game consists of multiple rounds. For example, in Guandan, the game progresses from 2 to Ace.
Trajectory Filtering. In this paper, we consider each observation-action pair of one step as a sample. We filter the generated data based on two criteria to obtain high-quality data. First, we only retain the
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observation and action data of the winning player. Additionally, we consider each observation ceservation-actioo pair from the player as a single data instance. Second, for all eight games, the environment provides the legal action options per action, and we only retain data samples where the number of legal action options is greater than one. The amount of data obtained after filtering presented in Table can also be seen from the table that the number of legal candidate actions per sample for Doudizhu, Guandan, and Mahjong exceeds that of the other five games, making these three games relatively more complex.
Supervised Fine- Tuning Data Generation. To perform instruction fine-tume ane-tuning on the model, we design prompts for each game convert observation-actio pairs into instructions and corresponding outputs. The instruction primarily consists of three parts: game introduction, state data, and output format instructions. The game introduction includes the game rules and the player's 's objectives. The state data comprises information such as the player's ' hand, community cards, the sequence of historical actions, and legal actions. The output format specifies that the model should output actions in JSON format. Complete instructions for each game can be found Appendix A.3
4 Experiments
4.1 Experiment Setup
Data. Through the data synthesis process described in Section 3.2 we obtain the training data for each game. For DouDizhu, GuanDan, and Riichi Mahjong, we sample 1,000k instances as training data. For Uno, Gin Rummy, Ledue Hold" emm, Limit Texas Hold' em, and No-limit Texas Hold'em, sample 400k instances as training data.
Model. We fine-tune three different types of language models Qwen2.5-78 --I--rutt 29 1- 8B-Instruct [30], and GLM4-9B-CHI 19]-to to analyze the impact of model type on performance. Additionally, fine-tune Qwen2.5 models with different parameter sizes, ranging from 0.5B to 14B parameters, to evaluate the effect of model size on performance. We fine-tune all models with LLLMA-Factory Framework (43) and use LoRA fine-tuning (44). The LoRA rank and LoRA alpha are set to 8 and 16, respectively. We fine-tune all models with epoch. We apply a peak of le-4 learning rate with cosine scheduler. The batch size is 128. We conduct experiments on a server with 8 H100 GPUs
Metric. For DouDizhu, we use the win rate. For GuanDan, we use the round win rate. For Riichi Mahjong, Uno and Gin Rummy, Ledue Hold' em, Limit Texas Hold' em, No-limit Texas Hold' em, we use reward score. The reward score in Mahjong is determined based on the average rank over multiple games, with the rewards for ranks 1, 2, 3, and 4 being 3, 2, 1, and 0, respectively. The reward scores for the other five games can be found in the RI.Card framework We evaluate the LLM by having it play multiple games against opponents. The number of games for the eight games are 1000, 20, 50, 500, 100, 1000, 1000, and 1000, respectively. For DouDizhu, following DouZero [7 we have the LLM play 500 times as the Landlord and 500 times as the Farmers, then calculate the average win rate for both roles.
4.2 RQ1. Can LLMs master complex card games? And how much data is required for them to master these games?
Experimental Design. We fine-tune the language model separately on each game's data and then evaluate its performance on the respective game. The training data information for each game is presented in Table For each game, we train on all training data for one epoch, saving a checkpoint every 400 steps. This allows us to analyze the model's performance changes with different amounts of training data. Additionally, we fine-tune three different types of L.LMs and five different sizes of LLMs to explore the impact of model type and size on performance.
Results and Analysis. The results of DouDizhu, GuanDan, and Mahjong are shown in Figure 2a Figure 2c As shown in the figure, with the increasing amount of training data, the performance of the L.M Doudizhu and Guandan gradually approaches that of the teacher model. In Mahjong, even though there is no available teacher model, the LLM has achieved performance comparable to
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Do.Datu
6.70
Deett teate GUM-----0
655
Data
Date
Own
(a) DouDizhu
(b) GuanDan
(c) Richi Mahjong
Figure Performance of different training data.
that of very strong Mahjong AI. Figure and Table have already shown that these three games have high complexity (long average decision steps and a large number of valid actions per step). These results indicate that, given sufficient high-quality data, LLMs can master complex card games. As training progresses, the model acquires more and more game strategic knowledge, leading continuous improvement in win rate. It worth noting that DouZero actually consists of three models, with one model trained for the Landlord and two models for the two Farmers. In contrast, LLMs can play all three roles with a single model, further demonstrating their powerful learning capability and versatility.
DouDizhu-Landlord
Farmer
0.50
Owen2.5 5-7.-- instruct
0.80
Dou-erceFaarme
0.50
Win Rate
Win
0.70
0.70
0.60
GLMA-BB-Chat
0.50
Doateer-Landterd
0.8
0.2
1.0
Dota
Data
(a) DoudizzuuLandlord
(b) DeuDizhu-Farmer
Figure 3: Performance of different roles.
Different Games and Model Types. As shown in Figure 2b and Figure 2cl in Guandan and Mahjong, there is no significant difference in performance among the three models, indicating that the learn- ing capabilities of the three models are comparable. However, in DouDizhu, the performance of GLM significantly worse than Qwen and Llama. To ana- lyze the performance differences of different models in DouDizhu, we further plot the win rates of the models when playing different roles, as shown in Fig- ure 3a and Figure 3b) As observed from Figure 3a and Figure 3b GL.M performs better than Qwen and Llama in Doul SouDDizuu-Landlrr while performing sig- nificantly worse than the two models in DouDizhu- Farmer. This suggests that GLM did not effectively balance the learning between the two roles and fo- cused more the landlord role, leading weaker performance for the farmer role.
DouDizhu
Rale
0.6
Quen2 1...e22.-0.B--aaee 3.S.B--uttrad
Qwee2: 5-1 1-155t--strutt
Qwee2. 7B-irsttuct
Qwer22 2.5 148-
0.4
Dou Dou
0.2
0.4
0.6
0.8
1.0
Data
Figure 4: Performance of models with differ- ent sizes DouDizhu.
However, comparing Figure 3a and Figure 3b we discovered another strange phenomenon: for DouDizhu, why is there such difference between Landlord and Farmer performance? And why are all of the models so much lower than ceiling performance for the Farmer role? Upon analyzing the training data we suspect that this is caused by the filtering rules applied to the game data. In DouDizhu, there one landlord and two farmers, with each game resulting in either a victory for the landlord for the farmers. During filtering, retained only the data for the winning side. When

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the farmers win, the data for both farmers is retained. However, in many cases, the victory may have been primarily driven by the actions of one farmer, while the data for the other farmer may be of lower quality. Consequently, the training data includes some low-quality data for the farmer role, leading to performance for the farmer role that falls significantly below its ceiling performance.
Different Model Sizes. We train and evaluate five different sizes of Qwen2.5 models on Doudizhu and the results are shown in Figure E From 0.5B to 7B, the performance of the models gradually improves the number of model parameters increases, indi- cating a positive correlation between model size and performance. However, we notice that despite the 14B model having the largest number of parameters, its performance is the worst.
Model
Landlord
Fanner
Average
Qwen2.5-77h 5-B-555--c-
0.828
0.784 0.570
0.806
Qwen2.5- 1-B-4t--sntttt
0.858
0.714
Table 2: Win rate of 7B and 14B models.
In order to analysis this, we further plot the win rates of the models when playing different roles in T From the table, 14B model performs better as the landlord (approaching the performance of the teacher model) but significantly worse as the farmer. This results in the average win rate of the 14B model being lower than that of the 7B model. This is similar to why the GLM model performs worse DouDizhu compared to Qwen and Llama.
4.3
RQ2. Can LLMs simultaneously master multiple games? Do different games mutually enhance each other or do conflicts arise between them?
Experimental Design. Based on the above experimental results, we roughly determine the amount of data required for each game to converge. We then sample data from the training datasets of each game according to this amount and merge them to obtain a mixed training set that includes data from all games. Specifically, the combined dataset contains 3.1 million data points, with the quantities of the eight games being: 700k, 950k, 650k, 200k, 50k, 250k, 200k, and 100k, respectively. We empirically determine the quantity of instances for each game based game complexity and the results of Experiment[4. 4.2) For example, games with higher complexity necessitate a larger volume of training data. We fine-tune the language model on this mixed training set evaluate whether it can simultaneously master multiple games. We compare the fine-tuned models with the API-based
models and the base models.
Model
DouDizhu
GuanDan
Riichi
Uno
Gin Rummy
Leduc
Limit
Nolimit
API-bused models
GPT-4o-mini
0.195
0.019
0.15
0.128
-0.176
0.30
0.45
2.47
GPT-4o
0.180
0.019
0.25
0.072
0.405
0.84
0.60
2.73
Gl-M-4-air
0.330
0.000)
0.10
-0.068
-0.415
-0.12
1.13
-0.89
GI.M-4-plus
0.345
0.019
0.00
0.020
-0.344
0.80
0.63
3.21
DeepSeek-V3 V3
0.320
0.000
0.15
0.128
0.147
0.77
0.22
0.18
DeepSeek-R
0.185
0.020
0.05
0.148
0.228
0.88
0.24
1.88
Base models
Owen2.5-7B-Innse
0.087
0.000
0.04
0.032
-0.530
0.63
1.05
1.25
Llama3.1-8BB I-Sttuuttttt
0.155
0.000
0.08
0.120
-0.463
0.62
-0.04
-2.10
GLM4-98B 9B-Chat
0.131
0.000
0.08
0.000
-0.362
0.52
0.85
-0.44
Fine-tuned models
Qwen2.5-7B. 70-..5-----Instuct--mn
0.852
0.634
1.08
0.108
0.177
1.24
2.66
4.86
Llama3.1-88 --S----sttucttmii 0.870
0.661
1.38
0.164
0.186
1.24
2.77
6.02
GLM4-9B PB--Chatmmi
0.882
0.698
1.31
0,252
0.191
1.24
2.89
5.77
Table 3: Performance of different models on all games. Bold font indicates the maximum value in each category, and underline indicates the second- -iihhet value. Mix refers to models fine-tuned on the mixed training set composed of data from all games.
Results and Analysis. The results are shown in Table B] All API-based models score relatively low on the two most complex games, GuanDan and Riichi, while their scores are relatively higher on the other six games. DeepSeek-R1 R1 performed the best among all API-based models, achieving the highest scores in three games. This demonstrates the effectiveness the reasoning mode. We observe that models of the same type with larger parameter versions reasoning versions perform better
7

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than those with smaller parameters non-reasoning versions. For example, DeepScek-Re shows improvements in most games compared DeepSeek- V3. GLM and DeepSeek's models score higher in DouDizhu, likely because this game is quite popular in China. Compared to the API-hased models, the three base models perform worse in most of the games. Compared to the API-based models and base models, the fine-tuned model achieve the best performance in most of the games. Notably, in the complex games of Doudizhu, Guandan, and Richi, their performance improve significantly. These results indicate that, after being trained on multiple high-quality game datasets, LLMs can simultaneously master multiple complex games.
Model/Game
DouDizhu
GuanDan
Ricch
Uno
Gin Rummy
Leduc
Limit
Nolimit
DouDizhu
0.806
0.010
0.08
0.032
-0.528
0.637
1.16
2.54
GunnDan
0.294
0.636
0.13
-0.004
0.030
0.637
1.10
2.62
Riichi
0.022
0.010
1.44
0.000
-0.233
0.637
0.91
-0.87
Uno
0.101
0.000
0.06
0.220
0.028
0.637
1.14
1,45
Gin Rummy
0.039
0,010
0.06
0.136
0.196
0.637
0.97
-0.34
Ledue
0.082
0.010
0.10
-0.032
-0.584
1.244
2.56
7.58
Limit
0.165
0.019
0.04
-0.008
-0.520
1.176
2.84
4.83
Nolimit
0.118
0.000
0.10
-0.056
-0.432
1.012
2.12
7.75
Mix
0.852
0.634
1.08
0.108
0.177
1.244
2.66
4.86
Table 4: Influence between different games using Qwen model. Each row represents the performance of model trained the mixed training set composed of data from all games. Bold indicates the maximum value, and underline indicates the second- second-highes, highest value, both excluding the mix model.
specific game across all games. Mix refers models fine-tuned
Model/Game
DouDizhu
GuanDan
Riichi
Uno
Gin Rummy
Leduc
Limit
Nolimit
DouDizhu
0.824
0.000
0.13
0.008
-0.496
0.637
1.14
3.21
GuanDan
0.463
0.598
0.15
0.112
-0.390
0.637
0.88
0.96
Riichi
0.142
0.000
1.42
0.060
-0.242
0.757
0.95
-1.07
Uno
0.234
0.000
0.04
0.160
-0.059
0.637
1.14
-0.47
Gin Rummy
0.073
0,000
0.06
0.112
0.208
0.637
-0.19
3.08
Ledue
0.172
0.000
0.10
0.052
-0.515
1.244
2.47
6.98
Limit
0.167
0.000
0.13
0.052
-0.469
1.105
2.84
6.86
Nolimit
0.170
0.000
0.04
0.056
-0.198
1.000
2.06
4.92
Mix
0.870
0.661
1.38
0.164
0.186
1.244
2.77
6.02
Table Influence between different games using Llama model.
Model/Game
DouDizhu
GuanDan
Riichi
Uno
Gin Rummy
Ledue
Limit
Nolimit
DouDizhu
0.723
0.010
0.10
0.060
-0.460
0.637
1.14
-1.21
GuanDan
0.447
0.629
0.02
0.136
-0.362
-0,068
-0.14
2.34
Riichi
0.063
0.000
1.33
0.052
-0.298
0.282
0.50
-5.15
Uno
0.111
0.000
0.06
0.176
-0.302
0.637
1.14
2.20
Gin Rummy
0.075
0.000
0.06
0.016
0.196
0.637
1.12
2.88
Ledue
0.142
0.000
0.08
0.048
-0.416
1.244
2.41
6.02
Limit
0.125
0.000
0.04
0.096
-0.411
1.232
3.02
5.05
Nolimit
0.114
0.000
0.06
0.004
-0.499
0.648
1.53
6.24
Mix
0.882
0.698
1.31
0.252
0.191
1.244
2.89
5.77
Table 6: Influence between different games using GLM model.
Influence Between Different Games. To explore the mutual influence between different games, we evaluate the model fine-tuned on one game across the other seven games. The results are shown in Table 6 Compared to models trained on other games, the model trained on GuanDan also performs well on DouDizhu. This indicates that GuanDan has positive influence on DouDizhu. Additionally, we can see that there are also positive influences among the three games, Leduc Hold's em, Limit Texas Hold' em, and No-limit Texas Hold' em. We claim that the rules of two games are more similar, there tends to be greater knowledge transfer between them, for example, DouDizhu and

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GuanDan, which have similar rules. Similarly, the three poker games, Ledue Hold' em, Limit Texas Hold'em, and No-limit Texas Hold'em em, exhibit more knowledge transfer due their similar game rules. Compared to DouDizhu and GuanDan, the rules of these three games are more similar, leading to more significant transfer effect. Therefore, game rules primarily dictate knowledge transfer between different games.
We also compare the models fine-tuned on a sin- gle game with those fine-tuned on all games. The comparison results for Doudizhu and Guandan are shown in Figure [ Because the card -ard-ng rules of Doudizhu and Guandan are very simi- lar, the performance of the mixed fine-tuned mod- els improves further on both games compared the models fine-tuned on each game individually. This indicates that Doudizhu and Guandan can mutually enhance each other's performance. How- ever, we also observe that the performance of the mixed fine-tuned models declined on the other six games compared to the individually fine-tumed models. This suggests that there is conflict be- tween Doudizhu and Guandan and the other six games.
Dizhu
1.00
Models
On Owan2: 5-7 BBBrmbuuct-ai single
Owm22 5-10 Instrcct
Limms: Poleetrutt-nnn
0.90
0.673
0.002
Llamed tratrctiiii
Win Palt
3.820
0.80
0.70
2.602
0.65
3.C360.CH
0.60
GuanDan
Figure 5: Comparison between models fine- tuned on a single game and models fine-tuned on all games.
Different Model Sizes. We train mixed models of different sizes Qwen2.5. Figure 6 shows the performance of these models on Doudizhu and Guandan. The performance improves the number of model parameters increases.
Win Rate for DouDizhu and GuanDan
Models
0.9
0.552
3.50
0.8
2.814
0.525
70
148
Win Bare
0.7
4.4 RQ3. Can LLMs maintain their general capabilities while mastering complex games?
0.6
0.5
Experimental Design. To test whether the mod- els lose their general capabilities after mastering the games, we evaluate the models' performance of general knowledge question answering, math- ematics, and coding before and after fine-tuning, using MMLU-Pro [12,, Math-500 (27). and Hu- manEval 28 benchmarks. general capabilities decline after fine-tuning on games, can further fine-tuning knowledge, mathematics, and coding data help restore these general capabilities to some extent?
0.4
DouDishu
GuanDan
Figure 6: Performance of models with different sizes on DouDizhu and GuanDan.
Results and Analysis of General Benchmarks. The results are shown in Figure The mixed models fine-tuned all games show significant declines in their abilities in snowledge-based ques- tion answering. mathematics, and coding. We then further fine-tune the game model on a mixed dataset composed of knowledge data, mathematics data, coding data, and game data. The proportions of these four types of data were 20k, 20k, 20k, and 8k, respectively. The 8k game data consists of 8 games, with 1k data points for each game. The quantities and proportions are chosen by re- ferring to previous work on general knowledge recovery [45]. The knowledge data, mathematics data, and coding data are taken from part of Tulu- 3's post-training data [46, as this model has made all its post-training data open source. The evaluation results of the model fine-tuned with the general data are shown in Figure As shown in the table, by further fine-tuning specific types of data,
Performance Comparison across Different Tasks
Modwls
Lonsl 1-4 sereectt
70
Lans) reruct
GLMS sa
60
GEMS 60 Char
GEMS nite geoel
50
20
MILLU-NO
Math-500
Average
Figure Evaluation results of different models on general benchmarks.

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the model can restore its ability in specific areas to some extent, as demonstrated in the paper with knowledge-basse -aaed question answering, mathematics, and coding capabilities.
Model
DiuDizhu
GuanDan
Ricchi
Uno
Gin Rummy
Leduc
Limit
Nolimit
LJama3.--8B-a -88-ntttttt Instruct-mix
0.870
0.661
1.38
0.164
0.186
1.24
2.77
6.02
JJama3.1-8B- --B--------tstct-mmi.. Instrnccttmi--egneeee
0,864
0.647
1.08
0.208
0.208
1.24
2.77
6.91
U. 882
0.698
1.31
0.252
0.191
1.24
2.89
5.77
GLM4-9B-Chat-mi -haeeni
0.874
0.645
1.38
0.152
0.205
1.24
2.89
6.65
Table 7: Performance of fine-tuned models on games. Mix refers to models fine-tuned on the mixed training set composed of data from all games. General refers to models fine-tuned on the mixed training set composed of the knowledge data, mathematics data, and coding data.
Results and Analysis of Games. To evaluate the impact of general data fine-tuning on game performance, we provid the performance of the models on all games before and after fine-tuning with general data in Table 7 From the table, it can be seen that the model's performance on games has remained mostly unchanged (slight improvements or stability in 5 games, and slight decreases in 3 games), indicating that the model regains certain level of general capability while maintaining its gaming ability.
Different Model Types. the three non-gaming benchmarks (MMLU-Pro, Math-500, and HumanEval), after fine-tuning on game data, GLM exhibites greater performance degradation compared to LLaMA. Furthermore, after fine-tuning on general-purpose data, GLM showes lower degree of recovery all three benchmarks relative to LLaMA, particularly on MMLU-Pro. This indicates that LLaMA better than GLM at maintaining general capabilities, especially in retaining general knowledge. This may be related to the differences in training data and training methods used by the two models.
4.5 Discussion on the advantages of LLMs compared to specialized game AI
Comparison of computation and data. We want to compare the amount of computation and data required for fine-tuning versus training a game Al system from scratch. However, due to the insufficient information disclosed about these game Al systems and differences in hardware environments, conducting a fair comparison is infeasible. Nevertheless, we have tried to list some comparative information in Appendix A This information does not directly demonstrate the advantages of L.MMs in terms computation and data requirements during training.
However, the key aspect aim to highlight is that the greatest advantage of LLMs lies in their nature as general-purpos learners. To achieve strong performance in games, both traditional reinforcement learning approaches and LLMs require the selection of appropriate game features. In this regard, both approaches are similar. Nevertheless, traditional reinforcement learning methods require the design of network architectures that are tailored to the chosen features. Different games employ different features, meaning that each game necessitates a specially designed network architecture- a step that often the most labor-intensive. L.Ms,, the other hand, eliminate the need for this step of network structure design and can be applied across all games without modification. We only need to design templates for each game. This flexibility is the foremost advantage of L.MMs. For example, DouZero, DanZero, and Mortal have network architectures specifically designed for individual games. DouZero even requires separate designs for the two roles in the game. In contrast, LLMs can perform well in three different games using the same architecture. Thus, we argue that the general learning ability of LLMs represents their most significant advantage.
5 Conclusion
In this paper, we explore the potential of large language models (LLMs) to master complex card games, evaluating their performance through fine-tuning on carefully selected high-quality gameplay interaction data. We explore three key research questions concerning LLMs' ability to master complex card games, their capacity to learn multiple games simultaneously. and the impact of game mastery on their general capabilities. Our study reveals that LLMs have the potential to achieve strong performance in complex card games, while also handling multiple games at once and retaining significant portions of their general capabilities.
10

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Acknowledgements
This research is supported by the National Natural Science Foundation of China (Grant No. 62406163).
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7 //nips http://cccpbbllcguuudoeeooo
Codeeuomissiiiieee Pollyy for more details.
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6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
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16

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For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
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We listed the computational resources used in the Experiment Setup 4.1
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We discuss societal impacts in Broader Impact C
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17

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20

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A Appendix
A.1 Comparison of computation and data
Among the three strong game Al models, Mortal, a Mahjong AI, does not have corresponding published papers, nor does its Git repository specify the computation and data required for training. Both DouZero and DanZero have published papers. Below is a comparison our fine-tuned models with these two models in terms of hardware environment, training time, and data volume:
DouZero: According to their paper, DouZero was trained on single server with 2 Intel(R) Xeon(R) Silver 4214R CPUs and 4 1080 Ti GP for 30 days; the data volume was not specified.
LLM-Dou: For the model in Figure 2a of our paper, we fine-tuned using single server equipped with Intel(R) (con(R) Platinum 8476C CPUs and 8 H800 GPUs on a dataset with million samples. The mue-tuning times for the three models are follows: Qwen2.5-7B: 11 hours; Llama3.1-8BB 12
hours; GLM4-9B: 14 hours.
DanZero: According their paper, DanZero trained on server with 4 Intel(R) Xeon(R) Gold 6252 CPUs and a GeForce RTX 3070 GPU for 30 days; the data volume was not specified.
LLM-Dan: For the model in Figure 2b of our paper, we fine-tuned using single server equipped with Intel(R) (R) Platinum 8476C CPUs and H800 GPUs on a dataset with million samples. The fine-tuning times for the three models are follows: Qwen2.5-7B: 21 hours; L-ama3,1-8B: 25
hours; GLM4-9B: 29 hours.
A.2 Evaluation on more general benchmarks
We provide the results of the models on four other common benchmarks (GQPA-Diamond, AIME2024, LiveCodeBench, IFEval) before and after fine-tuning on general mixed data in Ta- ble 8 From the table, can be seen that if the general mixed data does not include specific type of data, the model's corresponding capability will not be restored (after fine-tuning on the general mixed data, the performance of both models declined the instruction- cetructtoo-folowiing benchmark).
Model
MML.I-PPo
Math-500
HumanEval
GQPA-Diamond
AIME3024
LiveColeBench
IFEvsl
Avetige Pust_Thhree
Average ALL
Thme 1.tece---------B1 I-8t-nnstract
47.95
45.60
70:73
21.21
6.67
20.35
74:68
55.00
41.16
Liuna 3.1 Instract
35.20
46.97
Lluro-3.1 1.98-lnsrrtt-nii--eenre
47.30
10.00
52.54
G1-M-4-0--C
GLM-4-9B.4
41.00
47.22
Table 8: Evaluation results of different models on more general benchmarks.
A.3
Prompt Template
Figure 1: Prompt Template of DouDizhu
You now player in game of Dou Dizhu (Fight the Landlord). The game rules follows:
1. The game is played by three players with a standard 54-card deck including a red joker and a black joker.
+
2.
There are three roles in the game: landlord, landlord (farmer down of landlord), and landlord_up (farmer up of landlord).
3. After bidding, one player becomes the "landlord" who receives an extra three
+ cards. The other two players are the "peasants" who work together to defeat the landlord.
21

---

4. In each round, the starting player must play a card or valid combination of
cards.
5. The other players choose to either follow with higher-ranked card
+ combination, or pass.
6. two consecutive players pass, the round ends and the player with the
+ highest rank in that round starts the next round.
7. The objective is to be the first player to get rid of all the cards in hand.
The cards and comparison
follows:
Individual cards are ranked. Colored Joker Black White Joker Ace (A) 6 5 4 3.
+ King (K) Queen (Q) Jack (J) 10
2. The Rocket (Red Joker and Black Joker) and the Bomb are groups of cards that work differently in terms of play.
3. Compare only the Category. Compare only the Chains with the length. Compare the rank the Primal cards only. Jokers and non-consecutive cards.
+
4. The type of card combination: Solo, Solo Chain (5), Pair, Pair Chain (3), Trio, Trio Chain (2), Trio with Solo, Trio Chain with Solo, Trio with Pair, Trio Chain with Pair, Bomb, Four with Dual solo, Four with Dual pair.
Your task is to make the best decision each playing round. will provide you + with the following information:
Turn number:
%s
1. Your role:
%
2. Your current hand cards:
%
3. The union of the hand cards of the other two players:
Xs
4. The most recent valid
%
5. The played cards so far:
%s
6. The number cards left each player:
%
7. The number of bombs played far:
%
8. The historical moves:
%
22

---

9. The legal actions for the current move:
%
Please tell me what cards you want to play JSON format based on the provided + information. The JSON should contain an "action" key with value chose from legal actions.
If you choose play cards, the value should contain the array of cards you want play; if you choose to pass, the value should be empty array.
Output format examples:
Playing carda: {"action": [3, 3, 3}}
Passing: ("action"; []}
Please provide the corresponding JSON action based the given information.
Figure 2: Prompt Template of GuanDan
You are now player in game Guandan. The game rules are as follows:
1. The game is played by four players in partnerships, sitting opposite each other.
2. The deck consists of standard international decks with Jokers, totaling
108 cards.
3. The objective is to play higher combinations of cards to empty your hand
+ before your opponents.
4. If your team completes the game first, will advance in levels; the ultimate goal is to win Level A.
5. Card ranks in increasing order 2, 4, 5, 7, 9, Q, K, A. There are four suits (Spades, Hearts, Diamonds, Clubs) and four Jokers (two red, two black).
+
7. Players take turns in counterclockvise order, starting from a player who plays
any combination of cards.
8. Other players must play higher cards of the type higher combination,
+ or they must pass.
9. The game continues until three players have finished their cards.
10. Players are given titles based on the order they finish: Banker, Follower, Third, and Dweller.
The special cards and comparison Level Cards: The level number of the leading team determines the level cards. The level cards rank above aces but below jokers. For example, if the leading team is at level 6, then sixes are the level cards and rank above A.
follows:
1.
+
2. Wild Cards: The two level cards in hearts are wild. During the round, they be played any card, except jokers, to form combination with other cards. However, they only count as normal, non-wild cards when played as single card. For example, when the level in the round is 7, the 7 of hearts can make 4-bomb when combined with three Sa.
+
+
23

---

3. Normal Comparison: The normal comparison of the cards is from high to low in the order of red joker, black joker, A, K, q, J, 10, 8, 6, 5, 4, 3, 2. It applies when comparing with single card, pair, triple, tube, plate,
+ straight, bomb, and straight flush. Specially, full house compares the triple + in the combination only.
4. Bomb Comparison: Bomb depends on its number of cards. The amallest is 4-bomb of 2s and the largest is an 8-bomb of aces. However, a 5-bomb of 2s is larger than 4-bomb of aces. bomb ranks above: single card, pair, triple, tube, plate, full house, straight. straight flush is regarded or 5-card bomb, except the joker bomb. A bomb with or more cards ranks above a straight flush. Straight flushes rank according to their largest card regardless of suits. The joker bomb is the largest bomb in the game.
bomb that
ranks
The representation of cards card types follows:
1. #*Crrds==; Represented by two-character string, such as 'S2' which means + Spade 2. Detailed description below:
** Spades, Hearts, Clubs, and Diamonds are represented by the characters S, H, C, and respectively. Specifically, the suit for the small Joker is S, and for the big Joker, it H.
**Ranks**; A, 2, 3, 4, 5, 6, 7, 8, 9, 10, J, Q, are represented by A, 2, 3, 4, 5, 6, 7, 8, 9, T, J, Q, respectively. That is, the rank 10 is represented by the character т. Specifically, the rank for the small Joker is represented the character B, and for the big Joker, it is represented by the character For example, 'S2' represents Spade 2, 'HQ' represents Heart Qi 'SB' represents the small Joker, 'HR' represents the big Joker, 'PASS' indicates a pass.
2. +*Card Types**: [Type, Rank, Cards]
A card type is represented list of three fixed parts: Type, Rank, and Cards.
*Types*: type card combination, represented as string with possible values of ('Single', 'Pair', "Trips', 'ThreePair', 'ThreeWithwwo', 'TripsPair', 'Straight', 'Boom', 'PASS', 'tribute', 'back'].
**Rank**: The rank the highest card representative rank in the combination, with possible values ['A', '2', '3', '4', '5', '6', '7', '8', 19', 'T', 'J', 'Q', 'K', 'B', 'R', 'PASS'].
#*Carda**: The actual cards involved in the combination, represented as list.
Examples:
A single Diamond represented as: ['Single', '5', ['D5']]. A pair 4s represented as: ['Pair', '4', ['H4', ']4']]. PASS: ['PASS', 'PASS', 'PASS'].
Your task is to make the best decision in each playing round. will provide you with the following information:
1. Your position:
24

---

%
2. Your current hand:
%
3. Remaining cards of other players:
%
4. Last action of other players:
%
5. Last action of the teammate:
%s
6. Number of cards left for other players:
%
7. Cards played by the down player:
%s
8. Cards played by the teammate:
%
9. Cards played by the up player:
%
10. Self rank:
%
11. Opponent rank:
%
12. Current rank:
%s
13. Legal actions:
%
Please tell me your action in JSON format based on the provided information. The JSON should contain "action" key with value chose from legal actions.
Output format examples:
Playing card: ("action": ["Single", "9", ["H
Please provide the corresponding JSON action based the given information.
25

---

Figure 3: Prompt Template of Riichi Mahjong
You are now player in a game of Riichi Mahjong. The game rules are as follows:
1. The game uses 136 tiles divided into three suits (Pin, Sou, Wan) and honor tiles, which include wind and dragon tiles.
2. The tiles are mixed and arranged into four walls, two tiles high and tiles wide.
+
3. Players draw and discard tiles to form valid groups (mentau) of triplets
(Pon), sequences (Chii), or quads (Kan).
4. A hand be completed to declare win by forming four groups and pair.
5. Special rules include Riichi (declaring ready with closed hand) indicators (bonus tiles).
+
6. Players can call tiles discarded by others to make open seta, making their hands open or closed.
All possible actions 'dhaai: x', 'reach', 'chi chhi_lo'', chi mid', 'chi thi_high',
+ 'pon', 'kan', 'hora', 'ryukyoku', 'pass'. 'dhaai: x': discard tile x.
'reach': : declare a ready hand (riichi).
'chi_low', 'chi_mid', 'chi high'; Create meld by completing sequence, using the discarded tile.
'pon': create three-of-a-king meld using the discarded tile.
'kan': create four-of-a-kind meld. This can be done several ways: by adding
- tile to an existing three-of-a-kine meld, using discarded tile to complete a four-of -ak-ifd-- or declaring concealed four-of- a kind by having four identical tiles in hand.
'hora': declare win.
'ryukyoku': declare an aborted game or draw.
'pass': Opt not to take any action or declaration. This can mean passing on a chance to chi, pon, kan, or win (hora).
Your task is to make the best decision in each playing round. will provide you following information:
+
1.
Your identifier:
%
2. bakaze:
%
3. jikaze:
%s
4. kyoku:
%
5. honba:
26

---

Xs
6. kyotaku:
%
7. oya:
%s
8. Scores:
%
9. Your rank:
%
%
11. title left:
%s
12. shanten:
%
13. my hands:
%
14. wait tiles:
%
15. dora indicators:
%
16. dora owned:
%s
17.
akas in your hand:
%
18.
doras seen:
%
seen:
%
20. tiles seen:
%s
21.
ankan candidates:
%
27

---

22.
kakan candidates:
%
23. kawa overview:
%
24. fuuro overview:
%
25. ankan overview:
%
26. last tedashis:
%
27. riichi sutehais:
%s
28. last self taumo:
%
29. last kawa tile:
%s
riichi declared:
%
31. riichi accepted:
%s
32. can riichi:
%
33. is riichi:
%s
34. at furiten:
%
35. is
%
36. Legal actions:
%5
Please tell your action in JSON format based the provided information. The
+ JSON should contain an "action" key with value chose from legal actions.
28

---

Output format examples:
Playing card: {"action": "dahai: x"}
Please provide corresponding JSON action based on the given information.
Figure 4: Prompt Template Uno
You are now player in a game of UND. The game rules are as follows:
1. The game is played with a specially designed deck.
2. There are 2 players in the game.
3. Each player starts with cards dealt face down.
4. The top card from
Pile placed the Discard Pile to start
+ game.
5. Players take turns matching the card in the Discard Pile by number, color, or
+ symbol/action.
6. If player has no matching card, they must draw card from the Draw Pile.
7. If the drawn card be played, the player must play otherwise, they keep
+ the card.
8. The objective is to be the first player to get rid of the cards in hand.
The deck of UND includes 108 cards:
25 in each of four color suits (red, yellow, green, blue), each suit consisting of zero,
each through 9, and each of the action carda "Skip", "Draw Two", "Reverse".
The deck also contains four "Wild" cards and four "Wild Draw Four".
Action Wild cards have the following effects:
Skip: Next player the sequence misses
Draw Two: Next player the sequence draws two cards and misses turn.
Reverse: Order of play switches directions.
Wild: Player declares the next color to be matched.
Wild Draw Four: Player declares the next color to be matched; next player in
sequence draws four cards and misses
Your task is to make the best decision on your turn. I will provide you with the " following information:
Current step:
%
1. Your position:
%s
2. Your hand:
%
3. The top card in the Discard Pile:
29

---

%
4. Played_cards:
%
5. Number of cards left for each player:
%5
6. History actions of all players:
%
7. Legal actions:
%
Please tell your action in JSON format based on provided information. The + JSON should contain an "action" key with value chose from legal actions. The value should be an card or "draw" if you want to draw card.
Output format examples:
Drawing card: {"action": "draw"}
Please provide the corresponding JSON action based on the given information.
Figure 5: Prompt Template of Gin Rummy
You are now player in a game of Gin Rummy. The game rules are as follows:
1. The game is played two players using standard 52-card deck (ace is low).
2. The dealer deals cards to the opponent and 10 cards himself.
3. The non-dealer discards first. During each turn, you can pick up the discard
+ or draw from the face-down stock, then discard a card.
4. Players try to form melds of 3 or more cards of the same rank or 3 or more
cards of the same suit in sequence.
5. If the deadwood count (the value of non-melded cards) is 10 less, player
+ can knock. all cards can melded, player can gin.
6. player knocks or gins, the hand ends, and scores are determined. The - opponent can lay off deadwood cards to extend melds of the knocker.
7. The score is the difference between the deadwood counts. If the score is negative, the opponent receives the plus 25-point undercut bonus.
positive, the knocker receives it; if
8. neither player knocks or gins, they continue drawing discarding cards.
+ If the stockpile is reduced to two cards, the hand is declared dead.
All possible actions are: "draw_card",
"gin", "discard x",
"knock x", ideclare_deale "score N", "score s".
"draw_card"; Draw card from stockpile.
"pick_up baick_up_dsscarte discard"; Pick up the top card from the discard pile.
giin: Declare gin.
30

---

"discard x": Discard a card from your hand. "knock x": Knock card from your hand. ppeclare_dead"" Declare dead.
"score N Score player Current step: %s
"score s" Score player 1.
Your task is to make the best decision in each phase of the game, I will provide you with the following information:
1. Your id:
%s
2. Your hand cards:
%
3. Top card in the discard pile:
%
4. Other cards in the discard pile:
%s
5. Opponent known cards:
%
6. Left card number of stock pile:
%
7. History actions of all players:
%
8. Legal actions:
%
Please tell me your action in JSON format based on the provided information. The
+ JSON should contain an "action" key with a value chose from legal actions.
Output format examples:
Discarding card: ("action"; "discard 3S"}
Please provide the corresponding JSDN action based on the given information.
Figure 6: Prompt Template of Ledue Hold'en em
You are now player in a game of Leduc Hold' em. The game rules are as follors:
31

---

2. Your hand: 1. The deck consists of only two pairs of King, Queen and Jack (6 cards in
total).
2. There
players in the game.
3. The game has two rounds with a two-bet maximum.
4. Raise amounts are in the first round
in the second round.
5. In the first round, each player puts 1 unit in the pot and is dealt one card.
6. In the second round, one public card is revealed.
7. The winner is determined matching the player's card with the public card having the highest rank.
A11 possible actions are: "fold", "call", "raise", or "check".
Your task is to make the best decision in each betting round. will provide you with the following information:
Round number: %
1. Your position:
%
X
3. Public card (if in round 2):
%s
4. Your chips in the pot:
%
5. All chips in the pot:
%
6. Number of raises so far in two rounds:
%
7. History actions of all players:
%
8. Legal actions:
%
Please tell me your action in JSON format based on the provided information. The
JSON should contain an "action" key with a value chose from legal actions.
Output format examples:
Folding: ("action": "fold")
Calling: ("action"; "call"}
Raising: ("action"; "raise")
32

---

Checking: ("action": "check")
Please provide the corresponding JSON action based the given information.
Figure 7: Prompt Template of Limit Texas Hold'em em
You player in game of Limit Texas Hold em. The game rules
+ follows:
1. The deck consists of 52 cards.
2. There multiple players in the game.
3. Each player is dealt face-down cards (hole cards).
4. There are five community cards dealt in three stages: flop cards), the
+
turn (1 card), and the river (1 card).
5. There are four betting rounds: pre-flop, flop, turn, and river.
6. In each round, players can choose to "call", "check", "raise", or "fold".
7. This is fixed limit game, so raises are of fixed amount.
8. The number of raises in each round is limited 4.
vinner determined by the best five- hand using any combination of hole cards community cards.
+
Texas Hold' em hands are ranked from highest to lowest as follows:
Royal Flush: K, J, 10 all of the suit.
Straight Flush: Five consecutive cards of the suit. Higher top card wins.
Four Kind: Four cards of the same rank, Higher rank wins; same, compare + fifth card.
Full House: Three cards of one rank and two cards of another rank. Higher
three-card rank wins; if same, compare the two-card rank.
Flush: Five non-consecutive cards of the auit. Compare the higheat card,
+ then the second-hi second-highest, highest, and so on.
Straight: Five consecutive cards of different suits. Higher top card wins.
Three of a Kind: Three cards of the same rank. Higher rank wins.
Two Pair: Two cards of one rank and two cards of another rank. Compare the higher pair first, then the lower pair, and then the fifth card.
One Pair: Two cards the same rank. Compare the pair first, then highest so on.
+ non-paired card, then the second highest,
High Card: If no hand can be formed, the highest card wins. If the highest cards are the same, compare the second highest, and so on.
If the hands of equal rank, the pot is split.
A11 possible actions are: "fold", "call", "raise", or "check".
Your task is to make the best decision in each betting round. I will provide you with the following information:
Current betting round:
%
33

---

1. Your position:
%
2. Your hole cards:
Zs
3. Community cards:
%
Your chips pot:
%s
5. Al1 chips in the pot:
%s
6. Number of raises so far in four rounds:
%
7. History actions of all players:
%
8. Legal actions:
%
Please tell your action in JSON format based the provided information. The should contain "action" " key with value chose from legal actions.
Output format examples:
Folding: ("action": "fold")
Calling: ("action": "call")
Raising: ("action": "raise")
Checking: ("action"; "check")
Please provide the corresponding JSON action based on the given information.
Figure 8: Prompt Template of No-limit Texas Hold'em
You now player in game of No- domlimit Texas Hold's em. The game rules follows:
1. The deck consists of 52 cards.
2. There are multiple players in the game.
3. Each player is dealt two face-dovm cards (hole cards).
4. There five community cards dealt in three stages: the flop cards), the
4 turn (1 card), and the river card).
5. There are four betting rounds: pre-flop, flop, turn, and river.
6. In each round, players can choose to "call", "check", "raise", or "fold".
34

---

7. This is a no-limit game, so players can raise any amount from the minimum
raise up to their entire stack.
8. The number of raises each round is unlimited.
9.
winner determined by the best five-card hand using any combination of hole cards community cards.
+
Texas Hold' em hands are ranked from highest to lowest as follows:
Royal Flush: K, J, 10 all of the suit.
Straight Flush: Five consecutive cards of the suit. Higher top card wins.
Four Kind: Four cards of same rank. Higher rank wins; if same, compare + fifth card.
Full House: Three cards of one rank and two cards of another rank. Higher
three-card rank wins; if same, compare the two-card rank.
Flush: Five non non-consecutive cards of the suit. Compare the highest card + then second-highest and on.
Straight: Five consecutive cards of different suits. Higher top card wins.
Three of a Kind: Three cards of the same rank. Higher rank wins.
Two Pair: Two cards of one rank and two cards of another rank. Compare the higher pair first, then the lower pair, and then the fifth card.
One Pair: Two cards the
rank. Compare the pair first, then the highest non-paired card, then the second highest, and so on.
High Card: If no hand can be formed, the highest card wins. If the highest cards are the same, compare the second highest, and so on.
If the hands are of equal rank, the pot is split.
All possible actions are: "FOLD", CHELCKCCAL",, "RAISE_HAII POT",, "RAISE_POT", or + "ALL_IN".
Your task is to make the best decision in each betting round. I will provide you with the following information:
Current betting round:
%s
1. Your position:
%s
2. Your hole cards:
%s
3. Community cards:
%
4. Your chips in the pot:
%5
5. All chips in the pot:
%
35

---

6. Total chips of the pot:
%s
Remaining chips of all players:
Zs
8. History actions of all players:
%
Legal actions:
%s
Please tell me your action in JSON format based on the provided information. The JSON should contain "action" key with value chose from legal actions.
Output format examples:
Folding: ("action"; "FOLD")
Checking and calling: ("action": "CHECK_CALL")
Raising half pot: ("action": SAAISE_HAFEE (ACC__AAF__PO]] Raising pot: {"action": "RAISE_POT")
Raising all remaining chips: {"action"; "ALL, IN"}
Please provide the corresponding JSON action based on the given information.
B
Limitations
Although the language models achieved performance close to that of strong game Als, we found that the inference time of LLMs in games is relatively longer compared these Als. This is because these game Als often have a smaller number of parameters, while most language models in our experiments have parameter sizes in the billions (e.g., 7B). Although used LoRA fine-tuning reduce the number of trainable parameters, inference still requires calculating all the parameters, resulting in longer inference times.
C Broader Impact
This paper presents work whose goal is to advance the field of LLMs. Our work evaluates the learning capabilities of large models through games and does have negative societal impacts.
36


Reviews:
[{"review_id": "LOG1gBBhTl", "reviewer": "Reviewer_PGiq", "summary": "The paper conducts an empirical study to evaluate the capability of LLMs to handle card games. By giving the instruction prompt and supervisely fine-tuning LLMs with downstream datasets using LoRA, the paper shows that LLMs can master diverse card games and are able to simultaneously master multiple games by mixing trajectories together. Finetuning under a card game will hurt LLMs' general capabilities, while such decline can be marginally mitigated by integrating general instruction data.", "questions": "Descriptions of trajectory generation and the fine-tuning process are unclear; more details and a workflow figure might be more helpful.\nIn Figure 3, the authors pointed out that the reason for a low performance under Framer’s role in the DouDizhu game is due to the low quality of training data. While the assumption is reasonable, can the authors include further experiments to verify?\nThe authors talk about the influence between different games, starting from line 290, but did not specify the model they used for the experiment. All models should be tested to verify the conclusion.\nThe authors claimed that if the rules of two games are more similar, there tends to be greater knowledge transfer between them. However, while a model finetuned over GuanDan improved the DouDizhu’s performance, a model finetuned over DouDizhu still achieves a low performance on GuanDan. Does this imply that the transfer is not mutual?\nThe meaning of bolded values in tables should be mentioned in the caption.\nA typo in line 162. Instead of Table 2, it should be Table 1. \n\nI am mainly questioning the novelty and insights of this work. Whether the questions asked by the paper are valuable and whether the conclusion is general enough to be future development guidance.", "limitations": "yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "The paper implemented extensive empirical studies, covered diverse card games, and multiple models with various model parameter scales. Some reasonable observations and conclusions come from experiments. However, I have several concerns:\nThe paper focuses on card games, which are less complicated than certain chess games like Go, and primarily evaluates the existing methods instead of proposing a new model or training strategy, which leads to a relatively narrow scope with limited insights.\nThe fine-tuning process conducted in the paper is solely using LoRa with fixed hyperparameters, making the paper less comprehensive. More fine-tuning strategies should be discussed.", "was_revised": true, "final_justification": "The paper has some merit and responses indicate they can improve the current submission but the paper needs a resubmission with the revised results addressing some of the reviewer comments.", "rebuttal": "```\nW1: The paper focuses on card games, which are less complicated than certain chess games like Go.\n```\nThe table lists the complexity of eight card games and Go[1][2]. From the table, it can be seen that **multiple games have a comparable complexity to Go (InfoSet Number). Additionally, unlike Go, these card games are games of incomplete information**, with a significant amount of hidden information in each state (InfoSet Size), thus presenting more uncertainties when making decisions.\n| Game                   | InfoSet Number | Avg. InfoSet Size |\n|------------------------|----------------|-------------------|\n| DouDizhu               | 10^53 ∼ 10^83  | 10^23             |\n| GuanDan                | 10^118         | 10^36             |\n| Riichi Mahjong         | 10^121         | 10^48             |\n| Uno                    | 10^163         | 10^10             |\n| Gin Rummy              | 10^52          | 10^9              |\n| Leduc Hold’em          | 10^2           | 10^2              |\n| Limit Texas Hold’em    | 10^14          | 10^3              |\n| No-limit Texas Hold’em | 10^162         | 10^3              |\n| Go                     | 10^171         | 0                 |\n\n[1] Zha et al. RLCard: A Toolkit for Reinforcement Learning in Card Games\n\n[2] Wikipedia. Game_complexity\n\n```\nW2: I am mainly questioning the novelty and insights of this work. Whether the questions asked by the paper are valuable and whether the conclusion is general enough to be future development guidance.\n```\nLLMs have achieved outstanding performance in a variety of tasks and are being applied to more tasks. Therefore, assessing the learning potential of LLMs on complex tasks is of significant importance. \n\n**The complexity of games can be measured relatively accurately, giving the conclusions of this paper a certain level of generality.** By evaluating the learning potential of LLMs in complex games, this paper can, to a certain extent, infer the learning potential of LLMs in tasks with specific complexities, providing guidance for deploying LLMs in a broader range of tasks.\n\n```\nW3: The fine-tuning process conducted in the paper is solely using LoRA with fixed hyperparameters, making the paper less comprehensive.\n```\n**Full parameter fine-tuning requires higher computational resources, so we adopted the widely used efficient fine-tuning method, LoRA.** This also facilitates other researchers in reproducing our work. LoRA fine-tuning has also demonstrated the powerful learning potential of LLMs. We acknowledge that, when resources are abundant, full parameter fine-tuning and more hyperparameter analysis experiments would be beneficial.\n\n```\nQ1: In Figure 3, the authors pointed out that the reason for a low performance under Farmer’s role in the DouDizhu game is due to the low quality of training data. While the assumption is reasonable, can the authors include further experiments to verify? \n```\nCurrently, we manually check some data of Farmer’s role in DouDizhu to verify the existence of low-quality data. A more accurate approach would be to train a data quality classifier to filter out low-quality data before training the model and finally validate performance. Due to time and computational resource constraints, we will supplement this experiment in the future.\n\n```\nQ2: The authors talk about the influence between different games, starting from line 290, but did not specify the model they used for the experiment. All models should be tested to verify the conclusion. \n```\nTable 3 uses the Qwen model to analyze the influence between different games. Below are the influences between different games when using Llama and Glm. **The results of Llama and Glm show the same pattern as Qwen** (Line 292-299), where GuanDan has a positive influence on DouDizhu. Additionally, we can see that there are also positive influences among the three games: Leduc Hold’em, Limit Texas Hold’em, and No-limit Texas Hold’em.\n\nLlama: Influence between different games\n| Model     | DouDizhu    | GuanDan   | Riichi     | Uno         | Gin Rummy    | Leduc       | Limit      | Nolimit    |\n|-----------|-------------|-----------|------------|-------------|--------------|-------------|------------|------------|\n| DouDizhu  | **0.824**   | 0.000     | 0.13       | 0.008       | -0.496       | 0.637       | 1.14       | 3.21       |\n| GuanDan   | **_0.463_** | **0.598** | **_0.15_** | **_0.112_** | -0.390       | 0.637       | 0.88       | 0.96       |\n| Riichi    | 0.142       | 0.000     | **1.42**   | 0.060       | -0.242       | 0.757       | 0.95       | -1.07      |\n| Uno       | 0.234       | 0.000     | 0.04       | **0.160**   | **_-0.059_** | 0.637       | 1.14       | -0.472     |\n| Gin Rummy | 0.073       | 0.000     | 0.06       | **_0.112_** | **0.208**    | 0.637       | -0.19      | 3.08       |\n| Leduc     | 0.172       | 0.000     | 0.10       | 0.052       | -0.515       | **1.244**   | **_2.47_** | **6.98**   |\n| Limit     | 0.167       | 0.000     | 0.13       | 0.052       | -0.469       | **_1.105_** | **2.84**   | **_6.86_** |\n| Nolimit   | 0.170       | 0.000     | 0.04       | 0.056       | -0.198       | 1           | 2.06       | 4.92       |\n| Mix       | 0.870       | 0.661     | 1.38       | 0.164       | 0.186        | 1.244       | 2.77       | 6.02       |\n\nGlm: Influence between different games\n| Model     | DouDizhu    | GuanDan     | Riichi     | Uno         | Gin Rummy    | Leduc       | Limit      | Nolimit    |\n|-----------|-------------|-------------|------------|-------------|--------------|-------------|------------|------------|\n| DouDizhu  | **0.723**   | **_0.010_** | **_0.10_** | 0.060       | -0.460       | 0.637       | 1.14       | -1.21      |\n| GuanDan   | **_0.447_** | **0.629**   | 0.02       | **_0.136_** | -0.362       | -0.068      | -0.14      | 2.34       |\n| Riichi    | 0.063       | 0.000       | **1.33**   | 0.052       | **_-0.298_** | 0.282       | 0.50       | -5.15      |\n| Uno       | 0.111       | 0.000       | 0.06       | **0.176**   | -0.302       | 0.637       | 1.14       | 2.204      |\n| Gin Rummy | 0.075       | 0.000       | 0.06       | 0.016       | **0.196**    | 0.637       | 1.12       | 2.88       |\n| Leduc     | 0.142       | 0.000       | 0.08       | 0.048       | -0.416       | **1.244**   | **_2.41_** | **_6.02_** |\n| Limit     | 0.125       | 0.000       | 0.04       | 0.096       | -0.411       | **_1.232_** | **3.02**   | 5.05       |\n| Nolimit   | 0.114       | 0.000       | 0.06       | 0.004       | -0.499       | 0.648       | 1.53       | **6.24**   |\n| Mix       | 0.882       | 0.698       | 1.31       | 0.252       | 0.191        | 1.244       | 2.89       | 5.77       |\n\nEach row represents the performance of a model trained on one specific game across all games. Bold font indicates the maximum value, excluding the mixed training model. Italic font indicates the second-highest value, excluding the mixed training model.\n\n```\nQ3: The authors claimed that if the rules of two games are more similar, there tends to be greater knowledge transfer between them. However, while a model finetuned over GuanDan improved the DouDizhu’s performance, a model finetuned over DouDizhu still achieves a low performance on GuanDan. Does this imply that the transfer is not mutual?\n```\nFrom the results of the two games, DouDizhu and GuanDan, it can be seen that the transfer is one-way. However, from the results of the three games, Leduc Hold’em, Limit Texas Hold’em, and No-limit Texas Hold’em, the transfer is bidirectional. Compared to DouDizhu and GuanDan, the rules of these three games are more similar, leading to a more significant transfer effect.\n\n```\nQ4: Descriptions of trajectory generation and the fine-tuning process are unclear; more details and a workflow figure might be more helpful. The meaning of bolded values in tables should be mentioned in the caption. \n```\nBold font in Table 2 indicates the maximum value in each category. Bold font in Table 3 indicates the two largest values in each category. We will provide more details about trajectory generation and the fine-tuning process, as well as more detailed table captions."}, {"review_id": "odYg0lu9l5", "reviewer": "Reviewer_koKz", "summary": "The paper evaluates LLMs across eight complex card games by finetuning them on high-quality gameplay data. The paper investigates three research questions: (i) can LLMs master complex card games? (ii) Can they master multiple games simultaneously? And (iii) do their general-purpose capabilities degrade if they are fine-tuned on gameplay data? The paper conducts an empirical investigation of these questions, and the results show that LLMs can master complex card games, but their general-purpose capabilities deteriorate as a result of finetuning.", "questions": "* How do you ensure that the training and evaluation data are different?\n* Why do you attribute the poor performance of the 14B model in Figure 4 to overfitting and not some other problem? What does its performance on the training distribution look like?\n* Why do all the finetuned models obtain a score of 1.24 for Leduc in Table 2?\n* L121: Does a standard deck not have 52 cards?", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n* The research questions are sensible and interesting and form a complete study.\n* The experiments are comprehensive and executed well.\n* The paper is well-written, easy to follow, and contains sufficient information to reproduce the results.\n\n**Weaknesses**\n\n* Some claims are misleading, incomplete, or incorrect\n  * The paper claims that the base models “lack the strategic knowledge required for games, resulting in poor initial performance”. However, the paper provides no evidence that base models do not contain such “strategic knowledge”; it just shows that the models perform poorly when playing. Instead, the poor performance can be attributed to the “knowing-doing gap”, which was documented in other game environments (Paglieri et al., 2025; Ruoss et al., 2025) and states that LLMs perform poorly *even though* they contain a lot of (strategic) knowledge about these games.\n  * The paper claims that LLMs “can master multiple complex card games simultaneously” and that training on multiple games can “mutually enhance each other’s performance”. However, the results in Table 3 actually show the opposite, since the models finetuned on a single game almost always perform better than the models trained on data from all games (except for DouDizhu), and sometimes quite substantially so, even though they were trained on roughly the same amount of data (“the amount of data required for each game to converge”). Consequently, if I have a FLOPS budget of X, I will obtain a better performance by training eight individual models, one for each game, with roughly X/8 FLOPS per model, than spending the entire budget on finetuning a single model across all games. Thus, since the individual models score quite a bit higher than the joint one, the LLMs cannot “master multiple games simultaneously”, and since there is no (or very little) transfer between games, it is better to train individual models than a general one.\n* The experiment showing that some of the general-purpose capabilities can be recovered is ill-defined. Firstly, it is not very surprising that the performance of a fixed set of evals can be improved by finetuning on relevant data. However, the effects of that training process on other general-purpose capabilities are not shown. Secondly, the paper does not show how the model performs on the card games after some of the general-purpose capabilities have been “recovered”.\n* The paper studies imitation learning on imperfect information games, which is susceptible to delusions (Ortega et al., 2021).\n* The paper should discuss relevant related work on evaluating LLMs in game environments (Paglieri et al., 2025; Ruoss et al., 2025) as mentioned above.\n\nI am very happy to increase my score if these weaknesses and the questions below are reasonably addressed.\n\n\n**References**\n\nOrtega et al. Shaking the foundations: delusions in sequence models for interaction and control. 2021.\n\nPaglieri et al. BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games. ICLR 2025.\n\nRuoss et al. LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations. ICML 2025.", "was_revised": true, "final_justification": "The authors' rebuttal addressed most of my concerns, and the promised additional results and elaborations will greatly improve the paper. I have therefore increased my score.", "rebuttal": "```\nW1-1: The paper claims that the base models “lack the strategic knowledge required for games, resulting in poor initial performance”. However, the paper provides no evidence that base models do not contain such “strategic knowledge”; it just shows that the models perform poorly when playing. Instead, the poor performance can be attributed to the “knowing-doing gap”(Paglieri et al., 2025; Ruoss et al., 2025).\n```\n**The focus of our work is to analyze whether models can achieve relatively good performance in complex games through learning.** We have not further analyzed the reasons for the poor performance of the base model. The referenced work you mentioned provides another possible reason. Therefore, we modify the statement to: \"The poor initial performance is due to the base model's lack of strategic knowledge, or its inability to utilize existing strategic knowledge to make decisions in the environment.\"\n\n```\nW1-2: The paper claims that LLMs “can master multiple complex card games simultaneously” and that training on multiple games can “mutually enhance each other’s performance”. However, the results in Table 3 actually show the opposite, since the models finetuned on a single game almost always perform better than the models trained on data from all games (except for DouDizhu). \n```\nOur complete hypothesis is, **\"LLMs can master multiple complex card games simultaneously, with performance augmentation for games with similar rules and conflicts for dissimilar ones\"** (second finding in the abstract). We have also discussed this in detail in our experiments (Line 290-315). The point you mentioned, \"that training on multiple games can mutually enhance each other’s performance,\" only applies to games with similar rules.\n\nFirstly, \"LLMs can master multiple games simultaneously\" refers to our model compared to API-based models (Table 2), where the model trained on all games surpasses the best API-based models in most games.\n\nSecondly, as analyzed in our paper (Line 300-315), DouDizhu and GuanDan are similar and can enhance each other, whereas these two games have significant rule differences compared to the other six games. In mixed training, the data quantity for DouDizhu and GuanDan is larger, which benefits from mixed training. Therefore, the performance of these two games after mixed training is better than training individually (Llama and Glm from Figure 5). This has already demonstrated that mixed training helps \"LLMs can master multiple games simultaneously.\" We currently use only one heuristic data mixing ratio and believe that more refined adjustments in the future will allow the model to perform well across more games.\n\n```\nW2: The experiment showing that some of the general-purpose capabilities can be recovered is ill-defined. Firstly,the effects of that training process on other general-purpose capabilities are not shown. Secondly, the paper does not show how the model performs on the card games after some of the general-purpose capabilities have been “recovered”.\n```\n**General capability encompasses various aspects and currently lacks a precise definition.** Therefore, accurately measuring a model's general capability is relatively challenging. In our experiments, we investigated commonly used benchmarks from language model technical reports and selected typical benchmarks in three areas—knowledge-based Q&A, math, and code—as representatives to measure the model's general capability. In the future, we consider including other commonly used general capability evaluation benchmarks.\n\nBelow, we have supplemented the performance of the model on games after training with general data (with results before training listed for comparison). From the table, it can be seen that the model's performance on games has remained mostly unchanged (slight improvements or stability in 5 games, and slight decreases in 3 games), indicating that **the model has essentially regained a certain level of general capability while maintaining its gaming ability.**\n| Model                             | DouDizhu | GuanDan | Riichi | Uno   | Gin Rummy | Leduc | Limit | Nolimit |\n|-----------------------------------|----------|---------|--------|-------|-----------|-------|-------|---------|\n| Qwen2.5-7B-Instruct-mix           | 0.852    | 0.634   | 1.08   | 0.108 | 0.177     | 1.24  | 2.66  | 4.86    |\n| Qwen2.5-7B-Instruct-mix-general   | 0.854    | 0.544   | 1.06   | 0.236 | 0.220     | 1.24  | 2.82  | 5.50    |\n| Llama3.1-8B-Instruct-mix          | 0.870    | 0.661   | 1.38   | 0.164 | 0.186     | 1.24  | 2.77  | 6.02    |\n| Llama-3.1-8B-Instruct-mix-general | 0.864    | 0.647   | 1.08   | 0.208 | 0.208     | 1.24  | 2.77  | 6.91    |\n| GLM4-9B-Chat-mix                  | 0.882    | 0.698   | 1.31   | 0.252 | 0.191     | 1.24  | 2.89  | 5.77    |\n| Glm-4-9B-Chat-mix-general         | 0.874    | 0.645   | 1.38   | 0.152 | 0.205     | 1.24  | 2.89  | 6.65    |\n\n```\nW3: The paper studies imitation learning on imperfect information games, which is susceptible to delusions (Ortega et al., 2021). The paper should discuss relevant related work on evaluating LLMs in game environments (Paglieri et al., 2025; Ruoss et al., 2025) as mentioned above.\n```\nThank you for your suggestion. We will include discussions of these works in our related work section. As we mentioned in the introduction, a significant category of existing evaluations assesses model performance through prompting. The works by Paglieri et al., 2025, and Ruoss et al., 2025, fall into this category. Paglieri et al., 2025, evaluate both LLM and VLM in a gaming environment, whereas Ruoss et al., 2025, focus on assessing the impact of multimodal examples in context on performance. Ortega et al., 2021, emphasizes the LLM's ability to model causal relationships. Our work focuses on evaluating the learning ability of the model, hence there are substantial differences from these three works.\n\n```\nQ1: How do you ensure that the training and evaluation data are different?\n```\nEach game can control the initial hand data by specifying a random seed. We used different random seeds during training and evaluation to ensure that the hand data differed between these two phases.\n\n```\nQ2: Why do you attribute the poor performance of the 14B model in Figure 4 to overfitting and not some other problem?\n```\nWe have added further analysis of the 14B model and found that it failed to balance the learning of the two roles, resulting in an average win rate lower than that of the 7B model. Below are the win rates of the 7B and 14B models when playing as the landlord and the farmer and average win rates. From the table, it can be seen that the **14B model performs better as the landlord (approaching the performance of the teacher model) but significantly worse as the farmer. This results in the average win rate of the 14B model being lower than that of the 7B model.** This is similar to why the Glm model performs worse on DouDizhu compared to Qwen and Llama (Line 240-244). The 14B model did not effectively balance the learning between the two roles and focused more on the landlord role, leading to weaker performance for the farmer role.\n| Model                | Landlord | Farmer | Avg. Win Rate |\n|----------------------|----------|--------|---------------|\n| Qwen2.5-7B-Instruct  | 0.828    | 0.784  | 0.806         |\n| Qwen2.5-14B-Instruct | 0.858    | 0.570  | 0.714         |\n\n```\nQ3: Why do all the finetuned models obtain a score of 1.24 for Leduc in Table 2?\n```\nAs mentioned in Table 1, we used the official RLCard code to train a DQN model as the teacher model for Leduc. During testing, we found that the teacher model's performance on the validation set was also 1.24. Since Leduc has only six cards and two rounds of fixed-amount betting, we speculate that the teacher model learned some fixed strategies, and all three fine-tuned models have learned the strategies from the teacher.\n\n```\nQ4: L121: Does a standard deck not have 52 cards?\n```\nIn DouDiZhu, a standard deck of 52 cards is used along with a Red Joker and a Black Joker. We will update the statement in Line 121."}, {"review_id": "ZZ96wQg4AG", "reviewer": "Reviewer_Zt88", "summary": "This paper evaluates the learning ability of Large Language Models (LLMs) by fine-tuning them on a hight-quality dataset spanning 8 diverse card games, and compare their performances against the state-of-the-art agents like AlphaGo, AlphaZero, and MuZero. It also further examines how the fine-tuning impacts the models' general abilities and explore the strategies to restore the general capabilities of LLMs.", "questions": "1. The merged dataset spans from 50K to 950K samples per game. I wonder how sensitive is convergence to this imbalance? Have you tried balanced or curriculum sampling to ensure underrepresented games aren’t underfit?\n2. You show that mixed-dataset tuning recovers general capabilities, can you provide per-game win rates post-mix to confirm that individual game skills weren’t degraded?\n3. Why top reasoning LLMs are excluded? Including any of them could have clarified the true performance gap and dataset quality.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 2, "originality": 3, "strengths_and_weaknesses": "#### **Strengths**\n1. The authors utilizes strong game AIs to build a large, high-quality dataset that spans diverse scenarios.\n2. The paper is well-written, analyses and discussions are comprehensive and easy to read.\n3. Provides insightful experiments correlating fine-tuning data with in-game performance.\n4. Explores how high-quality fine-tuning impacts both specialized gaming skills and broader general capabilities, and also the possibility of recovering the general capabilities of LLMs.\n#### **Weaknesses**\n1. Only open models are being fine-tuned, though closed models cannot be fine-tuned by LoRA, it can still be fine-tuned with the produced dataset to verify the quality of the data.\n2. The paper omits comparisons with leading reasoning models, leaving the performance gap between the fine-tuned card-game models and top-tier baselines unquantified.\n3. The highly uneven distribution of game samples in the merged 3.1 M point dataset (ranging from 50 K to 950 K per game) may prevent the model from converging on all tasks simultaneously.\n4. After mixed-dataset fine-tuning restores some general capabilities, the paper fails to report per-game performance, so it remains unclear whether game-specific skills were preserved or sacrificed in the process.", "was_revised": true, "final_justification": "Supplementing the per-game win rates of the model after mixed-dataset tuning and including DeepSeek-R1 as baseline, act more as necessary justifications and supporting evidence that were previously missing, but is surely supports the claims of the paper. Thus, I would like to increase the clarity score as reward.", "rebuttal": "```\nW1: Only open models are being fine-tuned, though closed models cannot be fine-tuned by LoRA, it can still be fine-tuned with the produced dataset to verify the quality of the data.\n```\nThe open-source models demonstrated strong performance after fine-tuning on the produced dataset, which has already validated the quality of the data. Additionally, the cost of fine-tuning with closed models is relatively high. Therefore, we did not use closed-source models for fine-tuning.\n\n```\nW2: The paper omits comparisons with leading reasoning models, leaving the performance gap between the fine-tuned card-game models and top-tier baselines unquantified.\n```\nWe have added the results for DeepSeek-R1 below (along with results for the other two models for comparison). From the table, it can be seen that DeepSeek-R1 shows improvements in most games compared to DeepSeek-V3. However, our mixed training model still outperforms DeepSeek-R1 in most games. The reasoning model takes significantly longer to evaluate compared to models without reasoning, and due to current time constraints, we have only supplemented the results for DeepSeek-R1.\n| Model            | DouDizhu | GuanDan | Riichi | Uno   | Gin Rummy | Leduc | Limit | Nolimit |\n|------------------|----------|---------|--------|-------|-----------|-------|-------|---------|\n| DeepSeek-V3      | 0.320    | 0.000   | 0.15   | 0.128 | 0.147     | 0.77  | 0.22  | 0.18    |\n| DeepSeek-R1      | 0.185    | 0.02    | 0.05   | 0.148 | 0.228     | 0.88  | 0.24  | 1.88    |\n| GLM4-9B-Chat-mix | 0.882    | 0.698   | 1.31   | 0.252 | 0.191     | 1.24  | 2.89  | 5.77    |\n\n```\nW3: The highly uneven distribution of game samples in the merged 3.1 M point dataset (ranging from 50 K to 950 K per game) may prevent the model from converging on all tasks simultaneously.\n```\nWe determined the data volume for each game based on the results from training on individual games. It is indeed possible that during mixed training, the model may not converge on all tasks simultaneously.\n\n```\nW4: After mixed-dataset fine-tuning restores some general capabilities, the paper fails to report per-game performance, so it remains unclear whether game-specific skills were preserved or sacrificed in the process.\n```\nBelow, we have supplemented the performance of the model on games after training with general data (with results before training listed for comparison). From the table, it can be seen that the model's performance on games has remained mostly unchanged (slight improvements or stability in 5 games, and slight decreases in 3 games), indicating that the model has essentially regained a certain level of general capability while maintaining its gaming ability.\n| Model                             | DouDizhu | GuanDan | Riichi | Uno   | Gin Rummy | Leduc | Limit | Nolimit |\n|-----------------------------------|----------|---------|--------|-------|-----------|-------|-------|---------|\n| Qwen2.5-7B-Instruct-mix           | 0.852    | 0.634   | 1.08   | 0.108 | 0.177     | 1.24  | 2.66  | 4.86    |\n| Qwen2.5-7B-Instruct-mix-general   | 0.854    | 0.544   | 1.06   | 0.236 | 0.220     | 1.24  | 2.82  | 5.50    |\n| Llama3.1-8B-Instruct-mix          | 0.870    | 0.661   | 1.38   | 0.164 | 0.186     | 1.24  | 2.77  | 6.02    |\n| Llama-3.1-8B-Instruct-mix-general | 0.864    | 0.647   | 1.08   | 0.208 | 0.208     | 1.24  | 2.77  | 6.91    |\n| GLM4-9B-Chat-mix                  | 0.882    | 0.698   | 1.31   | 0.252 | 0.191     | 1.24  | 2.89  | 5.77    |\n| Glm-4-9B-Chat-mix-general         | 0.874    | 0.645   | 1.38   | 0.152 | 0.205     | 1.24  | 2.89  | 6.65    |\n\n```\nQ1: The merged dataset spans from 50K to 950K samples per game. I wonder how sensitive is convergence to this imbalance? Have you tried balanced or curriculum sampling to ensure underrepresented games aren’t underfit?\n```\nDue to the long training time and high costs associated with training on data from all games, we have currently only considered the most basic mixing strategy. Each game uses the data volume from when it converges during individual training to ensure sufficient learning for each game."}, {"review_id": "YOOQmpAYAn", "reviewer": "Reviewer_2Ka4", "summary": "This article is an empirical study that extensively explores the learning capabilities of Large Language Models (LLMs) in complex card games. The author systematically evaluates 8 different complexity levels of card games and studies the potential of LLMs to master complex games through supervised fine-tuning on high-quality game data. The experimental results indicate that: (1) LLMs can approximate the performance of strong game AI through supervised fine-tuning; (2) Compared to previous RL-Based AI, LLMs can simultaneously master multiple complex card games; (3) Mastering complex games leads to a decline in general capabilities, but this can be mitigated by integrating general instruction data.", "questions": "1. Could the authors provide a direct comparison of win rates between LLM-based and RL-based strategies? Currently, the comparisons mainly involve rule-based models.\n\n2. Have the authors considered evaluating the performance of reasoning-based models (such as O3, R1) on these card tasks?\n\n3. Have you considered directly applying reinforcement learning to LLMs for card tasks? What are the potential advantages of this approach?\n\n4. Regarding the performance decline of the 14B model, could you provide a more detailed analysis? For example, how does it perform on multi-task data? Can it better handle different tasks compared to the 7B model?", "limitations": "See Strengths and Weaknesses.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n\n1. Comprehensive experimental design: The author selected 8 card games of varying complexities, providing a comprehensive evaluation of LLMs' game learning capabilities.\n\n2. Detailed ablation studies: The paper conducted various ablation experiments, including performance of commercial models, the impact of model size on performance in supervised fine-tuning, the influence of training data volume, and the interplay between tasks in multitask learning.\n\n3. Practical value: Validates that LLMs with a parameter scale of 8 billion, after supervised fine-tuning with data generated by dedicated reinforcement learning AI, can achieve high performance in various card games.\n\n4. Multi-dimensional analysis: Not only evaluated game performance but also analyzed the impact on general capabilities, as well as the knowledge transfer effects between different games.\n\n5. Training cost comparison: The paper clearly compared the training resources and time costs between RL-Based and LLM-Based approaches.\n\n**Weaknesses:**\n\n1. Comparison benchmark limited: Primarily compared with rule-based models, lacking direct performance comparisons with original RL-based strategies.\n\n2. Lack of reasoning model evaluation: Although understandable given time constraints at the project's initiation, there is a lack of evaluation of current mainstream reasoning models (such as O3, R1, etc.), which could be supplemented when time permits.\n\n3. Unexplored potential of reinforcement learning: For card games with clear rules and rewards, the direct possibility of applying reinforcement learning to LLMs has not been explored. This could be an important research direction and may not require additional experiments.\n\n4. Insufficient explanation for abnormal performance of the 14B model: The author attributes the performance decline of the 14B model to overfitting, but this explanation lacks persuasiveness and requires a more in-depth analysis.\n\n5. Why can't general knowledge be trained together with the card game tasks? Will the sequence of training first on card games and then on general tasks reduce the performance of the card game tasks?\n\n6. In the limitations section, there is no detailed explanation of the differences in reasoning time.", "was_revised": true, "final_justification": "4 Borderline accept", "rebuttal": "```\nW1: Primarily compared with rule-based models, lacking direct performance comparisons with original RL-based strategies.\n```\n**Currently, the original RL-based strategies in multiple games are position-bound during training, meaning they can only be deployed in specific positions.** In five out of the eight games, reinforcement learning was used to generate data as teacher models (DouDizhu, GuanDan, Leduc Hold’em, Limit Texas Hold’em, No-limit Texas Hold’em). For GuanDan, the original RL-based strategies only provided pre-trained models for specific positions (a model trained for position 1 and position 3, with no models for position 2 and position 4; positions 1 and 3 form the first team, while positions 2 and 4 form the second team). In Leduc Hold’em, Limit Texas Hold’em, and No-limit Texas Hold’em, the models pre-trained using the RLCard default code were also trained for position 1. Therefore, the pre-trained models for these four games can only be used in position 1, which is why we did not make comparisons with the original RL-based strategies. For DouDizhu, DouZero[1] also trains different models for different positions and does not compare with its own strategy, but rather with other methods. We followed DouZero's comparison setup.\n\n[1] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning\n\n```\nW2: There is a lack of evaluation of current mainstream reasoning models (such as O3, R1, etc.), which could be supplemented when time permits.\n```\nWe have added the results for DeepSeek-R1 below (along with results for the other two models for comparison). From the table, it can be seen that DeepSeek-R1 shows improvements in most games compared to DeepSeek-V3. However, our mixed training model still outperforms DeepSeek-R1 in most games. The reasoning model takes significantly longer to evaluate compared to models without reasoning, and due to current time constraints, we have only supplemented the results for DeepSeek-R1.\n| Model            | DouDizhu | GuanDan | Riichi | Uno   | Gin Rummy | Leduc | Limit | Nolimit |\n|------------------|----------|---------|--------|-------|-----------|-------|-------|---------|\n| DeepSeek-V3      | 0.320    | 0.000   | 0.15   | 0.128 | 0.147     | 0.77  | 0.22  | 0.18    |\n| DeepSeek-R1      | 0.185    | 0.02    | 0.05   | 0.148 | 0.228     | 0.88  | 0.24  | 1.88    |\n| GLM4-9B-Chat-mix | 0.882    | 0.698   | 1.31   | 0.252 | 0.191     | 1.24  | 2.89  | 5.77    |\n\n```\nW3: Unexplored potential of reinforcement learning. Have you considered directly applying reinforcement learning to LLMs for card tasks? What are the potential advantages of this approach?\n```\nCurrently, we have only evaluated the learning capability of LLMs through supervised instruction fine-tuning. In the future, we will explore whether reinforcement learning methods like GRPO can further enhance the learning ability of LLMs. The advantage of GRPO lies in its ability to automatically explore the chain of thought reasoning process between observations and card playing. Additionally, we are considering adopting an online reinforcement learning approach, where the first step involves using SFT to enable the model to grasp basic game strategies, and the second step involves using RL to allow the model to directly explore and learn in the environment online.\n\n```\nW4: The author attributes the performance decline of the 14B model to overfitting, but this explanation lacks persuasiveness and requires a more in-depth analysis.\n```\nWe have added further analysis of the 14B model and found that it failed to balance the learning of the two roles, resulting in an average win rate lower than that of the 7B model. Below are the win rates of the 7B and 14B models when playing as the landlord and the farmer and average win rates. From the table, it can be seen that the **14B model performs better as the landlord (approaching the performance of the teacher model) but significantly worse as the farmer. This results in the average win rate of the 14B model being lower than that of the 7B model.** This is similar to why the Glm model performs worse on DouDizhu compared to Qwen and Llama (Line 240-244). The 14B model did not effectively balance the learning between the two roles and focused more on the landlord role, leading to weaker performance for the farmer role.\n| Model                | Landlord | Farmer | Avg. Win Rate |\n|----------------------|----------|--------|---------------|\n| Qwen2.5-7B-Instruct  | 0.828    | 0.784  | 0.806         |\n| Qwen2.5-14B-Instruct | 0.858    | 0.570  | 0.714         |\n\n```\nW5: Why can't general knowledge be trained together with the card game tasks? Will the sequence of training first on card games and then on general tasks reduce the performance of the card game tasks?\n```\nIt is possible to train using both general data and game data together. Since we obtained a model trained on all data in our second experiment, we explored whether adding a small amount of general data to this model could restore its general capability. In the future, we will analyze the performance changes when training with both general data and game data together.\n\n```\nW6: In the limitations section, there is no detailed explanation of the differences in reasoning time.\n```\nGame AIs compared in our papers often have a smaller number of parameters, whereas most of the language models in our experiments have parameter sizes in the billions (e.g., 7B), which results in longer inference times."}]

Author rebuttal:

Meta-review: The paper investigates whether LLMs can master complex card games. The authors fine-tune multiple open-source LLMs on high-quality gameplay data across eight games, showing that fine-tuned LLMs achieve competitive performance. The study is comprehensive and provides open-source resources, which I believe are valuable.

The major concerns are that the novelty is limited, as the method mainly relies on SFT. However, given the extensive experiments and the released source code, I think the main contribution lies in its empirical insights and is acceptable. Another issue is that the claims about mastering multiple games are somewhat overstated, and the authors should carefully revise these claims in the final version. During the discussion, the authors provided further analyses and clarifications, which helped to convince some reviewers.

Overall, I find this work to be valuable in the community and recommend acceptance, with the expectation that the authors carefully refine their claims in the final version. In addition, since the main contribution of this paper is empirical, I strongly encourage the authors to release all data, code, and trained models.
Decision: Accept (poster)