Mixing Expert Knowledge: Bring Human Thoughts Back To the Game of Go
Authors: Yichuan Ma
Linyang Li
Yongkang Chen
Peiji Li
Jiasheng Ye
Qipeng Guo
Dahua Lin
Kai Chen
Venue: NeurIPS 2025 poster ()
Keywords: LLM reasoning
Reinforcement Learning
Go
Primary area: deep_learning

Abstract: Large language models (LLMs) have demonstrated exceptional performance in reasoning tasks such as mathematics and coding, matching or surpassing human capabilities. However, these impressive reasoning abilities face significant challenges in specialized domains. Taking Go as an example, although AlphaGo has established the high performance ceiling of AI systems in Go, mainstream LLMs still struggle to reach even beginner-level proficiency, let alone perform natural language reasoning. This performance gap between general-purpose LLMs and domain experts is significantly limiting the application of LLMs on a wider range of domain-specific tasks. In this work, we aim to bridge the divide between LLMs' general reasoning capabilities and expert knowledge in domain-specific tasks. We perform mixed fine-tuning with structured Go expertise and general long Chain-of-Thought (CoT) reasoning data as a cold start, followed by reinforcement learning to integrate expert knowledge in Go with general reasoning capabilities. Through this methodology, we present LoGos, a powerful LLM that not only maintains outstanding general reasoning abilities, but also conducts Go gameplay in natural language, demonstrating effective strategic  reasoning and accurate next-move prediction. LoGos achieves performance comparable to human professional players, substantially surpassing all existing LLMs. Through this work, we aim to contribute insights on applying general LLM reasoning capabilities to specialized domains. We will release the first large-scale Go dataset for LLM training, the first LLM Go evaluation benchmark, and the first general LLM that reaches human expert-level performance in Go.

Mixing Expert Knowledge: Bring Human Thoughts Back To the Game of Go
Yichuan Ma Linyang Li Yongkang Chen', Peiji Li Jiasheng Ye², Qipeng Guo Dahua Lin', Kai Chen',
SShnnghai AI Laboratory, Shanghai,
School of Computer Science, Fudan University, Shanghai
yichuanm24@mm.fug www.m..daad.uuu edwaaaneddeenn. piaabaornpla
Abstract
Large language models (L.LMs) have demonstrated exceptional performance reasoning tasks such as mathematics and coding, matching or surpassing human capabilities. However, these impressive reasoning abilities face significant chal- lenges in specialized domains. Taking Go established the high performance ceiling AI systems in Go, mainstream LLMs still struggle to reach even beginner-level proficiency, let alone perform natural language reasoning. This performance gap between general-purpose purpose LLMs and domain experts is significantly limiting the application of LLMs a wider range of -omaii--pecific tasks. In this work, we aim to bridge the divide between LLMs' general reasoning capabilities and expert knowledge in domain-specific tasks. We perform mixed fine-tuning with structured Go expertise and general long Chain-of- Thought (CoT) reasoning data as cold start, followed by reinforcement learning integrate expert knowledge in Go with general reasoning capabilities. Through this methodology, we present LoGos, powerful LLM that not only maintains outstand- ing general reasoning abilities, but also conducts Go gameplay in natural language, demonstrating effective strategic reasoning and accurate next-move prediction. Lo- Gos achieves performance comparable to human professional players, substantially surpassing all existing LLMs. Through this work, we aim contribute insights on applying general LLM reasoning capabilities to specialized domains. We will release the first large-scale Go dataset for LILM training, the first L.M Go evalua- tion benchmark, and the first general LLM that reaches human professional-leve performance in Go at: https: // /i thub. com/ thub..com/Entarcchaan.. /tt///aarrrcnannLoooo...
example, although AlphaGo has
1 Introduction
Large language models (LLMs) exemplified by OpenAl-ol and DeepSeek-RI R1 [Jaech et al., 2024, Guo et al., 2025] have demonstrated remarkable performance on reasoning tasks such as mathematies and coding, reaching surpassing human expert capabilities. However, when focusing on specialized domains with searce corpora, LLMs reach boundaries in their reasoning capabilities. the Go game, back in 2016, AlphaGo [Silver et al., 2016 6] has already demonstrated the extraordinary potential of AI systems. AlphaZero [Silver al., 2017] further challenged human intelligence by establishing that AI systems could "master the game of Go without human knowledge". In stark contrast, existing general LLMS perform far below even beginner level in Go. This stark contrast between specialized Al systems and general-purpose LLMs has become a significant constraint limiting LLMs' applications across broader scenarios.
the case of
1 Cooresponding Author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Galta GlentEletaan
15
100%
kotedl Humonn.-- -t
13
15
11
100%
Demp
LoGos
Chunddd P. Seronet
0's
60%
100%
Percentage
Wn
Losa
Figure 1: Win rates of LoGos against various models. For general LLMss, we select DeepSeek- R1, ol-mini, and 7--onntt for game- play. Additionally, several specialized Go models. The KataGo- HumanSL model series [Wu, 2019] is designed to simulate human players at different skill lev- els. MataGo--umanSL-11 emulates intermedi- ate amateur players, while CataGo-HumanSe 9d mimics top amateur and professional players. is a well-known Go AI model that achieves performance comparable mid-level professional players.
Response
First, I need to comprohensively analyzo the current board state, By observing the first moves, can see that both black and white have the upper left E14, and D12; White has responded with D13, DIt, and D9. This area is currently in active contention...
play LoGos against
positions all corners.
Black has formed structure with
R14 is also a reasonable choice, focusing more on the local upper right corner, advantages, especially considering that White has already invested resources the lower right corner.
more
Given game state, the model first analyzes the situation: "In the upper left corner: Black has formed structure... and then choose the valid next move R8.
Figure 2:
Achieving expert-level performance in specialized domains with scarce pre- pre-training corpora presents considerable challenges for general large language models. The scarcity of domain-specific data makes virtually impossible for general LLMs to acquire Go strategies and master the specialized terminology through pretraining alone. Consequently, approaches based direct distillation of general LLMs or pretraining on large-scale nomain-speciffi natural language corpora [Liu et al., 2025, Wu et al., 2024] face limitations in the context of Go. Unlike tasks such mathematies and coding, which benefit from abundant natural language corpora and human reasoning examples, specialized tasks like Go typically offer only structured domain-specifie professional data that be obtained scale. Although some works have demonstrated that LLMs can complete professional tasks such as Chess through structured prediction [Feng et al., 2023, Schultz et al.. 2024], model that produces only structured outputs still falls short of the general reasoning model we aspire to develop.
If the necessary condition for achieving natural reasoning is introduce large amount of human reasoning data, then for a field like Go where such data is scarce, integrating professional capabilities into general model will inevitably require extensive annotation efforts. This raises critical question: Does it mean that incorporating specialized domain capabilities into general models is destined to remain unresolved challenge?
To this end, we explore an alternative approach and discover viable pathway forward. We assume that while general LLMs perform inadequately on unseen tasks due their lack of specialized knowledge, they still demonstrate some reasoning generalization capabilities on novel tasks, attempting to understand, reason, and think based on task descriptions. Furthermore, while specialized domains like Go often have limited natural language corpora, they typically possess substantial amounts of structured domain knowledge. Therefore, we propose novel training paradigm, which first injects domain expertise into general models through heuristically constructed expert-level synthetic data at scale, and then aligns this knowledge with the model's inherent reasoning capabilities.
Through heuristic rules, we construct large-scale expert-level Go dataset, comprising a next-step prediction dataset based structured domain- specifcc information and Go commentary dataset.

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Subsequently, we mix this specialized Go dataset with long Sfain-of-thhogghe (CoT) reasoning data from domains including code and math for fine-tuning, achieving both the injection of domain expertise and the cold start of long CoT reasoning capabilities. Following this cold start phase, we employ Group Relative Policy Optimization (GRPO) [Shao al., 2024b], encouraging the model to self-explore using long CoT'' reasoning forms for the next-step prediction task in Go.
As expected yet interestingly, through such reinforcement learning (RL) process, the fine-tuned model spontaneously self-explores and develops a stable strategy capable of natural reasoning and prediction based on correct understanding of board states. Our model achieves performance comparable to professional Go players, becoming the first general LLM to reach this level of proficiency. Further, the model maintains outstanding performance on general reasoning tasks such as math and code, becoming a strong generalized Al model that expert models such as AlphaGo cannot be. Our experiments conclusively demonstrate that general LLMs can acquire expert-level capabilities in specific domain like Go, while maintaining general performance. We firmly believe this method can be applied to other domains and anticipate its broad application prospects. As alpha the first letter in the Greek alphabet, AlphaGo heralded the beginning of new era in artificial intelligence. In this work, we name our model LoGos (Language-Orient Oriented Go System), which means "word" and "thought" in Greek, aspiring to bring the uniquely human capacity of reasoning back to the ancient game Go. In summary, this paper makes the following contributions:
1. We introduce LoGos, general LLM that achieves both expert-level proficiency in the domain of Go and outstanding performance in general reasoning tasks, demonstrating that LLMs can attain expert-level performance in specific domains while maintaining general reasoning capabilities.
2. We propose a training strategy that enables general models to achieve expert-level perfor- mance in specific domains using only structured domain-specitn data which is scalable. We thoroughly validate the effectiveness of this strategy on Go.
3. We collect, construct, and release the first large-scale Go dataset specifically designed for LLM training, well as an evaluation benchmark on Go designed for assessing LLMs.
2 Mixed Learning of Go
In this section, we detail methodology for enabling general large language models absorb expert-level ability in domains exemplified by the game of Go.
We begin by outlining approach to modeling the next step prediction task within the Go domain. Subsequently, based on our collected and curated large-scale Go dataset, we construct a large-scale Go professional dataset covering two tasks: next move prediction and commentary. This dataset is consisted of a 10-million-scale next-step prediction dataset constructed using heuristic rules, alongside 100K scale commentary dataset.
Following this, we mix the Go professional dataset with general soagg--hai-of-thhuuuee (CoT) rea- soning data from various domains such as code and mathematics. We fine-tune our base model on this mixed dataset to cold-start both Go-specific expertise and long-form CoT reasoning capabil- ities. Using this fine-tuned model as the starting point for reinforcement learning, we encourage self-exploration through carefully designed queries and rewards that promote long CoT reasoning on the next move prediction task. We design a segmented reward function to encourage the output of relatively optimal predictions long COT format. In this way, exemplified by the Go game, LLMs can self-explore expert-level reasoning chains.
2.1 Modeling the Game of Go
We first introduce our approach to modeling the next move prediction task in Go. As a highly complex board game, Go is played on 19x19 grid, where black moves first and players alter- nate placing stones until the game ends. The complexity of board games can be quantified as b², where b denotes the average number of
Given the gane nove 1ist belox: 1.X-Q16 2.0-D16 141.X-J7 Please pradict the next move.
Figure 3: Prompt demonstration for modeling the
Go game as a next move prediction task.
3

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legal moves per position and d represents the typical game length. With parameters of b = 250 and d = 150, Go's complexity substantially exceeds that of other board games like chess (b 35, d R 80), presenting significantly more challenging task.
In this work, we formulate the entire sequence of moves in Go game as the input sequence, and the model's 's task is to predict the next move, Specifically, for Go position after A moves, if we denote the preceding move sequence as (anJ then for a model TTs the prediction task can be characterized as:
Tk To (T1, -
Each move position a combination of letter and number coordinate. To distinguish between the two players, we use X and O to denote black and white stones, respectively. Therefore, given a game record sequence, the model provided with a query as demonstrated in Fig.
2.2 Expert-Level Go Dataset Collection
We introduce large-scale professional Go dataset, comprising Go commentary training dataset and professional Go dataset constructed using heuristic rules for the next-step prediction task.
Next Step Prediction Dataset We collect
dataset containing over 5 million game records played by both top amateur and professional Go players. From these game records, we uni- formly sample over 10 million game states and annotate them using the open-source Go en- gine KataGo [Wu, 2019]. As Go engine that employs Monte Carlo Tree Search (MCTS), KataGo's output for given game state includes the top 10 most probable next moves, subse- quent variations for each move, and numerical analysis of the current move. We use KataGo to annotate these 10 million game states and design the following heuristic template to construct data for model training as seen in Fig. 4.
<Step1> (player identity determination) The 141.X-J7. next player
last white.
<Step2> (amalysie of several potential next noves) Try 142. -42.0-C10, the subsequent possible variation would be: ... <Stap3> (aummary) Considering analysis, the best nove is 142.0-C10. <Stept> (structured output)
the above
Next playar: White Next position: 010
vin rate: 52.4 X
Figure 4: Examples of the next step prediction dataset. The heuristic template consists of four parts: (i) confirming whether the next player is black or white; (ii) analyzing several possible next moves; (iii) summarizing and selecting the optimal next move; and (iv) structured output.
Commentary Dataset We collect and pro- cess 100K Go commentary cases from open re- sources, each containing an independent game state and the corresponding comment. We pro- cess the commentary data into a training dataset where, given a game state, the model's task is to provide logically sound commentary with correct terminology usage according the current game state.
2.3 Mixed Cold Start
We mix the professional Go dataset with long CoT reasoning data from domains including math- ematics and code, performing mixed fine-tuning on the base model to serve as the starting point for GRPO. After fine-tuning, LoGos demonstrates the capability to complete next-step prediction tasks and provide commentary based on the current game state. Simultaneously, the model maintains outstanding performance on other general reasoning tasks.
2.4 Seff-Exploration with Reinforcement Learning
Based on our analysis of the fine-tuned initial model, we observe that it spontaneously generalizes reasoning capabilities acquired from long COT reasoning data to the Go prediction task. When presented with instructions resembling those in CoT reasoning contexts, the model naturally performs understanding, analysis, reasoning, and summarization for a given game state. However, at this stage, the initial model still cannot apply the professional knowledge acquired from the professional Go

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Query
wre professional Go player
onelyse bowrd position based given gomo recorel suggot
several polestial nest moves with analyiis, explore posaile variations that could follow, provide thoughtfll reczoning, and finelly summarize by recommending the bosf next move. in the gives gone, X reprosents black otense and
represents white stones.
Long Reasoning Data
LXX-066 Z.O Oi0 X.X A.O 5XX 6. CST.XXXXXX---CO CA &O CA 9. TO D3 T.X
12, 12.0-FI7 1IX. DI M.D- 15XX 1.16.
RI 17.3-R9 18. 484 1. BS 20,0 QH4 XLX 22. PID XLX 04 24, 4D 253 BIS 26.0 P-5 27 OU? 28.0 229 29
30,0 SEX
SLY 3A,
26 36.
38.0
39
X-X 40.O SIA ALX 42.0-BUI
Cold Start Data
433X-33
65.X-O1
72.0 73X.J3 74.0-K5
X.OS
80.0-09
SIX-KA 02.0-LA S5XXJS
85.X-77 86.0 87.X-110 85.0- 89. 90.0 MB 9LX 92.0 NJ P3.X-10 94.0-C8
Fine-Tuna
Go Professional Data
Please prodict and onelyse the next move.
Response
Firtt need to anelyse bey features the current postiion. White has just played ca, which is clearly
Blokks loft-idde formation, White's intuntion with this tertfory.
wooken Block's influencv
patentially proparing
possible
responses!
Initial Model
Firetly, Black playing at a direct resporse, almad af protecting lafraide space and praventing further While invesion. 86 con commect with Blook's presious stene ot C6 forming refotively selid strecture. If White choosce to counter at Da, Block can continse strongthoning thiir posliion with ES, creating powerful shope. Such a formation not only resiets potential White invosions but olso helps Block ostablish more influosce the left side.
Solf Explore
as la more direct choice, more dractly confronting White's stonss C4 and CS. This approach might lead to more Block establish
fighting. could building shope.
side. White
DO, Black
ground countera Backk might need additional stops
represents
BS, potentially offering different and connolidare structure.
paint. Howaver,
P
Through coledtatio and comperionn of the possible oufosmos those oplions, 16 oppeors the must reosenabloo choice. not only directly addresses White's threat and protects Blocks space on the left sids, but olso helps Black astuhlish morn structure. According to my coloslations, chossing B6 might give Black approsimuttly 51.0% wis rate, which i failly good advantage the current position.
Next move?
Export
Additionnll, has the benelit dimiltanooslly uddrecsing multiple diroctions, incrossing Blook's averall fleatbiliyy White Heathilly la particularly valuabla
Blokk
building shope with then further adjust based pasition.
resposse.
Figure 5: Our methodology for integrating Go professional capabilities with LLMs' long COT reasoning abilities. After mixed cold start and GRPO training, our model ultimately successfully transfers the reasoning capabilities acquired from long CoT data Go tasks. For given query, the model correctly performs analysis, thinking, reasoning, and summarization, ultimately selecting a reasonable next move.
dataset, resulting in inaccurate prediction results. To address this issue, we introduce a reinforcement learning phase to enhance the model's performance on the next-step prediction task. We uniformly sample from the KataGo annotated data described in Section 2.2, construct queries according to the game states, and utilize annotation information as metrics for reward calculation.
2.4.1 RL Optimization with GRPO
We utilize widely used RL method, GRPO (Group Relative Policy Optimization), which is proposed in DeepSeek-Math. For given problem-ansee
pair (g, a), GRPO samples group of independent responses {o:);- from the old policy new- Each output is scored by reward model or reward function, yielding G rewards r = {ri, (r1,72,... TG] correspondingly. GRPO optimizes the LLM by maximizing the following objective:
JORPO (0) = Blg- P(Q): (or)
Taad (0|g)
G
1
min
Ag
Ace, clip
1,1 e, 1 +
And]
DDx (nolltte
(2)
G
TBald
where A, Ti-man 1o--mean(rr.. Pa....F) is group-relative advantage for the i-th response.
2.4.2 Reward Modeling
Specialized for the Go task, we design a reward function that rewards both high-matching moves and responses that predict the win rates accurately. We sample subset from KataGo's 's annotated data, and for the top 10 options ranked by KataGo's win rate, we obtain and sort their win rates {w; 110 denoting the highest win rate as w". We stipulate that the model's output must include the predicted next move position along with a win rate estimation for that prediction. The win rates predicted by the model are denoted as ( [- Based on this design, for a given model output o,, the corresponding reward T; is calculated using piecewise reward function:
5

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011
1+:: [w
81 9
if rank(() =
( 01
1+51 [+ [w:
8i (1)
0r
1+B2( -w;)
(w -1o)
if rank(i) [2,3]
Ti
02
01
1+9 -1
02
1+3x( w
Bg(n" -10) 10,)
if rank(i) E [4, 10]
01
02
if rank(i) & [1, 10] A format correct
otherwise
Here, 01, 022, 31, B2, C1. C22 C3 are all adjustable parameters, satisfying ei > C3 ag- We heuristically set Ci = 0.8, 02 = 0.6, C3 = 0.4 to explicitly reward moves with higher matching degrees. We set predicted next move. Meanwhile, 02 set to 0.2, with B2 = 10, to reward relatively more matching predictions within the same ranking level.
0.1, 31 = 10 to reward responses with accurate win rate estimations for the
With an external reward model such as KataGo, we collect structured expert-level knowledge. which is rather easy in expert-level reasoning tasks such as exploring scientific discoveries, data analysis, or controlling systems in the industrial process. By self-exploring the policies with RL training, the hard-to- hard-to-generalize generalize expert knowledge can be utilized in a general, powerful reasoning-sty LLM.
3 Experiments
3.1 Experimental Setup
For the selection of long CoT reasoning data, we collect several distilled reasoning datasets covering wide range of general tasks including code, mathematics, and general reasoning. Specifically, our collected datasets include Openthoughts- 14KK 114K (Team, 2025], NuminaMath-Qwes CoT-5M [Team et al., 2025), Reasonnnnnn [Ahmad et al., 2025], Bespoke-Stratos- 17k [Labs, 2025], and DDDeepSeekRR--Dssilllee
Datasets
[Zhao et al., 2025].
our original benchmark for measuring LLMs Go capability. KataGo-Benchh- IK is a test set of 1,000 samples from KataGo annotation data, with game states sampled across various player skill levels. For each position, a prediction is considered correct if the predicted move falls within the annotated candidate moves. Additionally, we measure the model's general performance on the following comprehensive benchmarks: GPQA-Diamond [Rein et al., 2024], BBEH [Kazemi et al., 2025], KOR-Bench [Ma et al., 2024a], AIME, MATH [Hendrycks et al., 2021], and LevcCodeBench [Jain et al., 2024]. These benchmarks collectively evaluate model performance across major reasoning domains, including commonsense reasoning, scientific knowledge, logical reasoning, mathematics, and coding abilities.
Benchmarks We propose KataGo-Bench-
Models For foundation models, we conduct training based on the Qwen2.5 series (Yang et al., 2024], including Qwen2.5-7B-Baz and Swen2.5-32B-Baase For general performance comparison baselines, we select Owen2.557BBInnstee
while also comparing with DeepSeck-RI-Distill-Oe -wB and DeepSeek-RR -eli-iiilll--ee-e-eee [Guo et al., 2025] to thoroughly demonstrate our models' outstanding performance on general tasks. Furthermore, we evaluate several mainstream closed-source and open-source models for comparison, including DeepSeek- R1, OpenAI-ol -miii, and Claude3.7-Sonnee We also compare Go professional capabilities with the RataGo-Human-- Specifically, this model series encompasses skill levels from 18k (kyu) to 9d (dan), with 27 distinct proficiency levels arranged sequentially from 18k to 1k, 1 to 1d, and 1d to 9d, where 18k represents beginner-level players and 9d represents top amateur and professional standards.
and Sreen..5-3BBInstteee
model series [Wu, 2019], which is trained human player game records.
3.2
Main Results
Table presents our primary experimental results. First, both our 7B and 32B model achieve Go- specific performance that significantly surpasses all existing general LLMs. On KataGo- BeaGoo-Benc--1e the strongest general model apart from LoGos is 7--onee,--Snnet, which achieves prediction accuracy of 34.3%. However, even this performance falls substantially below that of the lowest- ranked beginner-level Go specilaized model, KataGo-HumanSL 188k (67.4%). In contrast, our

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Table I: Main results on Go professional benchmark and general benchmarks. We evaluate and compare the models' performance on KataGo-Bench-I IK and several general reasoning benchmarks, with the evaluation results on general benchmarks obtained using Opencompass [Contributors, 2023].
The score on BBEH is calculated by averaging the results from each subset.
Model
KataGo-Rench
General Benchmark
GPQA
LiveCodeBench
Closed-source Models
DeepSeck RI
17.6
69.7
44.5
78.3
86.7
97.6
83.8
ol-mini
27.3
61.1
70.16
56.7
95.0
75.0
Claude3 7-Sonnet
33.8
64.8
30.0
79.8
63.2
Open Models (7B)
Qwen2.5-7B. Base
1.4
28.3
9.9
20.4
13.3
83.2
9.0
Qwen2.5.77 5.B. Instruct
39.9
12.9
42.8
3.3
92.6
16.2
ReepSeckRR--iaiiee Rell-isill--een-77 Qwen-7B
41.4
13.1
55.9
33.3
88.2
20.4
LoGos(7BB
88.1
37.9
22.1
65.7
40,0
93.2
23,4
Open-source zource Models (32B)
Qwen2.55328-Base
15.6
42.8
90.7
13.8
Qwen2.5-32RR 2--n2.25-2R-
46.0
18.0
58.1
20.0
95.2
29.9
DeepSeck RI Distill-ween Qwen-32B
4.7
56.1
27.4
70.0
46.7
94.5
36.5
LoGos(32BB)
88.6
63.6
34.1
74.8
56.7
96.5
50.9
Go Professional Models
KataGo-HumannSL HumanSL-188
67.4
KataGooHuumaSSL
79.7
KataGo-Humann HumanSI-S1
85.5
KataGo-HHumnSSLee
87.8
(a) Self-Exploration
(b) without Start
(c) Start with Naive Fitting
7B-Instruct
Model
3239 Instruct
R1-3201
Heunstic Rude
90
Hanan BL
Performanca (%)
Performence (%)
Performance
80
80
70
Human St
60
800 Training Steps
1200
1500
200
400
600
800
1000
300
600
900
1500
GRPO Training Steps
GRPO Traiing Steps
Figure 6: Experiments model self-exploration in the professional Go task. (a) Performance curve during reinforcement learning from our initial point in the main experiment; (b) Performance when applying reinforcement learning directly to instruction models without cold start; (c) Comparison of reinforcement learning performance when replacing heuristic rule construction with direct prediction during cold start.
models achieve nearly 2.6 times the accuracy of Claude3.7 7-nnnnet and even exceed the performance of SataGo-HumanSS.. (88.6% 6% and 87.8%), indicating that our models attain proficiency in Go comparable professional players.
To verify that LoGos maintains strong general capabilities, we conduct comprehensive evaluations various general benchmarks. The results demonstrate that LoGos not only excels in Go-specific tasks, but also maintains impressive performance across diverse general tasks, including mathematics, coding, and commonsense reasoning, achieving leading performance within similar model sizes. Compared to the Qwen2.5 series Instruct models, LoCios consistently outperform across all general tasks evaluated. Even when compared with -i--reeee-----isii-----------
and DeepSeck-RI-M R--isttll-- Qwen-32B, our models achieve comparable performance across general tasks, surpassing the distilled models on most of the benchmarks. These results conclusively demonstrate that our models have attained expert-level proficiency in Go while preserving outstanding general capabilities.
3.3 Experiments on Self-Exploration
3.3.1 Self-Exploration in Go
We analyze the performance growth of L.LMs Go through reinforcement learning. In Fig. 6(a), we plot the performance curves during GRPO training, using mixed fine-tuned Qwen2.5-7BB Swen2.5-7B-Base and
7

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Qwen2.5-32B- Base as initial points. At the beginning of training, the models' initial performance ranges between 60% and 70%, comparable beginner-level. As the training progresses, both of the models quickly discover a stable reasoning strategy through self-exploration, and their perfor- mance rapidly increases, surpassing the HumanSS- model (representing an medium-level player). Subsequently, the models' performance continues to improve throughout the training process, eventually exceeding the MataGo-HumanSL-0 model, which simulates top human players and professional players, ultimately achieving proficiency level comparable to professional Go players. The experimental results presented above demonstrate that after the cold start phase, LLMs are capable of exploring reasonable strategies for Go-related tasks through self-exploration.
3.3.2 RL without Cold Start
Given that models can self-explore in Go tasks, a natural question arises: can LLMs discover stable strategies through self-exploration without requiring a cold start? To investigate this question, we select the Bree2.5-7B-Instruue and Swen2.5-32B-Instreee 7B and Ri-bseei-RR-Diiill--------e
along with initial points, and directly apply GRPO training the Go task. As seen Fig. 6(b), our results confirm that although models can improve prediction accuracy throuth self-exploration, the upper limit is significantly lower without cold start, the models ultimately fail to reach the beginner-level performance (67.36%). Notably, the self-exploration ceiling for DeepSeck's distilled models is lower than that of Qwen's instruction models, demonstrating that cold starting with long reasoning data alone does not directly result in effective generalization in domain-speciff tasks like Go. Based these observations, conclude that general reasoning data alone is insufficient, and domain- specffcc expertise is necessary for effective performance in specialized tasks like Go.
---DSsek----sstill---
3.3.3 Cold Start with Naive Fitting
While we have proved the significance of cold start, the necessity heuristic rules when constructing expert-level dataset still needs verification. To investigate this, try completely removing heuristic rules and use direct prediction as a substitution. For a given board state, the response directly predicts the position of the next move. As shown in Fig. 6(c), results on the 7B-size model show that when using naive fitting for cold start, the model fails to properly acquire Go expertise from the professional dataset, resulting in significantly lower capability ceiling (below 50%) compared with using heuristic rules (88%). These results confirm that in the cold start phase, a well-structured and relatively natural domain-specific expert-level dataset is crucial for helping LLMss raise their exploration ceiling during the self-exploration stage.
4 Discussion
4.1 Ablation Study on Reward Design
Since our reward function is heuristically designed, it re- quires further investigation. We conduct RL experiments with several alternative reward designs: (i) rewards given only to the top-1 move; (ii) rewards given only to the top-3 moves without distinguishing; and (iii) tiered processing of moves without intra-level rewards (i.c., setting aı and a2 to zero in Equ. 3).
95
Original
Top1 Only
Level Only
TopS Only
Performance (%)
Human SL 9d
85
80
Human SL 1d
75
As shown in Fig. 7, models perform effective self- exploration under different reward settings. However, un- der the top-1 and top-3 reward designs, the sparse reward distribution results in lower performance compared to the tiered reward and our original reward function. Comparing the original reward function with the tiered reward approach, significant difference observed, though our reward design ultimately achieved a higher performance ceiling, demonstrating the advantage of smoother reward function.
300
600
900
1200
1500
GRPO Training Steps
Figure 7: Ablation reward desion
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GRPP--Stp--00
ERPO Blop 1000
KataGo 9d
Table 2: Human evaluation results the generated responses, analyzing whether the predicted move is correct, and whether the corresponding explanation is correct, incorrect, or ambiguous.
Performance (%)
80
60
Move
Explanation
Correct
Incorrect
40
Correct 121
4
Ambiguous
60
20
0-50
51-100
101-150 151-200
200+
Incorrect
37
3
Move Range
Figure 8: Analysis on move range
4.2 Case Study on Response Quality
We invite several top amateur Go players to evaluate the generated responses based on move prediction accuracy and explanation quality. As shown in Tab. 2, our model produces accurate move predictions in 96.5% of cases, with 55.6% of the explanations correctly given. However, there exist cases with incorrect explanations or ambiguous descriptions, which we will discuss briefly below.
Firstly, Go contains more than 600 terms, each corresponding to specific local pattern on the board. Our model occasionally exhibits issues with incorrect usage of these specialized terms. Moreover, in certain scenarios, the correctness of the explanation becomes hard to assess, For examplc, during the opening phase, outputs such as "establishing strong positional influence" represent a generally applicable description that, while broadly valid, offers limited informational value.
4.3
Context Curse for LLM Performance in Go
4.3.1
Problem Description
As shown in Fig. the game state as the game lengthens. For a given game sequence {x,)) J. any substitution between elements F; and x, could possibly create entirely different board positions. As the sequence length grows, the board structure becomes increasingly complex, requiring the model to capture more connections, which leads to decreased prediction accuracy. In contrast, models that directly represent the current board state, like KataGo, maintain performance regardless of game length.
observe that it becomes increasingly challenging for LLMs to comprehend
4.3.2 Solution to context curse
To address this issue, we further explore encoding methods for the game records and design strategy that incorporates 2-D board state information directly into the input query. Specif- ically, for a given move list, we provide the model with the resulting board state. This state is represented as 19x19 2-D array, where the values 1, -1, and 0 denote a black stone, a white stone, and an empty intersection, respectively. Our Python im- plementation based on an open-source Go board rendering repository https: // github........ Hammm//aaaHH///// hte//gobbbooo board.
100
Kaaiodd
Alcoces--ap Gepp-
Performnce (%))
40
20
040
55-100
101-150
151-250
200+
After modifying the query format and following the same exper- imental procedure (cold start + RL), we surprisingly find that the new approach significantly mitigates the model's "context curse" problem. Fig. 9 presents the model's performance under the new format across various input sequence lengths.
Move Range
Figure 9: Analysis on move range (with 2D board rendering applied.)
evident that with this updated input, the model achieves markedly more accurate understanding of the board state, maintaining high prediction accuracy even for sequences exceeding 200 moves. Therefore, we conclude that this new method (2-D board state rendering) effectively resolves the context curse challenge.

---

4.4 Experiments on Data Mixing Ratio
4.4.1 Impact of Data Mixture Ratios on RL Performance
We first investigate the impact of the data mixture ratio of Go dataset during the cold-start phase. While holding the training for the general reasoning dataset epoch, we compare the performance of models initialized with I versus 2 epochs of Go data. The models' performance is evaluated on the KataGo- Bench- K benchmark throughout the subsequent RL stage.
Performance (%)
Mixing Epoch Go
90
Mixing 2 Epoch Go
80
70
As shown in Fig. 10, the model initialized with 2 epochs Go data exhibits better performance at the initial point. However, as the RL process unfolds, this model does not demonstrate superior learning capabilities. In contrast, the model trained on single epoch of Go data ultimately achieves higher perfor- mance ceiling. Guided by this finding, and also considering the
60
250
500 750 1000 1250
GRPO Training Steps
Figure 10: RL performance of dif- ferent Go data mixing ratios. reduced computational requirements, we adopt the one-epoch setting for our cold-start experiment.
4.4.2 Impact of Data Mixture Ratios on Generalization Performance
We then test the model's performance on general benchmarks after training for one epoch, mixing fixed amount of reasoning data and varying quan- tities of Go data. As demonstrated Tab. 3, incor- porating larger volume of Go data does not cause a significant degradation in the model's general reasoning capabilities.
Table 3: Performance on general benchmarks with varying amounts of Go data.
Dataset
Without Go
500K
2M
10M
MATH
97.0
97.1
96.2
97.4
97.4
AIME OmniMath
73.2
72.3
72.5
72.9
65.0
65.1
66.4
66.3
66.3
GPQA diamond
61.9
61.2
60.1
59.6
60.6
5 Related Works
LLM as Game Player Previous works have shown that LLMs demonstrate capabilities in various gameplay tasks. Through Chain-of-Thought (CoT) reasoning [Wei al., 2022], LLM-based agents constructed through prompt design can complete gameplay tasks such as Pokémon battles, StarCraft competitions, and Minecraft strategy simulations [Ma et al., 2024b, Hu al., 2024, Madge and Poesio, 2024, Shao et al., 2024a, Gong et al., 2023, Chen et al.. 2024]. However, tasks like Go are relatively more specialized, and general L.LMs perform even worse than even beginners, rendering the approach of building agents for Go based on general LLMSs. Some studies have explored the possibility of training L.LMs learn strategies for games such as chess and poker [Feng et al., 2023, Zhang et al., 2025, Huang al., 2024]. Specifically for chess, further works have demonstrated that templates constructed with heuristic rules can help LLMs learn the trajectories and strategies generated by professional models in chess tasks [Wang et al., 2024, Schultz et al., 2024].
Domain Expert AI In specialized domains, exemplified by AlphaGo and AlphaFold, AI systems have demonstrated expert-level capabilities [Silver et al., 2016, Jumper al., 2021, Meng et al., 2024, Silver et al., 2017]. Building upon the powerful general capabilities exhibited by LLMs, several stud- ies have explored methodologies for transforming LLMs into domain experts. A common paradigm involves synthesizing solutions to specialized problems into learnable data for language models based on heuristic rules. Leveraging tools such as the Lean language, works like AlphaGeometry [TTinh et al., 2024] and DeepSeek-Prover [Xin et al., 2024] have investigated approaches to equip LLMs with the capability of solving Olympic competition-level problems.
Conclusion
We present LoGos, general large language models that achieves expert-level Go proficiency while maintaining outstanding general performance. We introduce robust method to mix expert knowledse mowleegee into general L.MSs and demonstrate the potential of LLMs to acquire expert-level reasoning abilities specific domains. We believe that the future, such a mixing-expert-knowlees method can lead to wider range of LLM applications in the AI industry.
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The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Question:
Answer: [Yes]
Justification:
Instructions given human Go players are illustrated in Appendix C.3,
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then much detail as possible should be included the main paper.
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15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country institution) were obtained?
Answer: [Yes]
Justification: See Appendix C.3.
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The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
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16. Declaration of LLM usage
Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy (https: //tp:///ee....eeeee Ceeeurips.cc/oofeeeeee /LLM) for what should or should be described.
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A Limitations
First, we will discuss the limitations of this work. Although our method effectively and robustly integrates domain-specific expert-level knowledge into general LLMs, this approach still has several necessary preconditions for application. Taking Go as an example, our method requires a scalable source of professional knowledge, such as KataGo. Further, we need to design heuristic rules based on professional knowledge and construct cold start synthetic data that LLMs can learn from and understand, thereby training initial point for self-exploration. In other specialized domains, quiring such scalable professional knowledge might necessitate the introduction of domain-spenn speciifcc models/databases, while the design of heuristic rules should also consider the specific task require- ments. Therefore, this method of mixing- -eweeeeepeeeweeeeeeeeeee researchers in specialized fields to enable broader applications.
inevitably requires deep cooperation with
Furthermore, despite the remarkable effectiveness of self-exploration, its training speed relatively slower. Consequently, compared with traditional AI models in Go. LoGos is trained on significantly fewer game states during the RL phase. therefore believe that the autoregressive architecture of LLMs might actually constraint the training efficiency when applied to tasks requiring real-time reasoning, such as Go.
B Experimental Details
B.1 Experimental Settings and Compute Resources
In the mixed cold start phase, we SFT the base models with maximum sequence length of 32.768 tokens. We employed a cosine annealing learning rate scheduler with rates ranging from 4c-5 to 4c-6. For training the 7B model, utilized 32 A800 (80GB) GPUs, while the 32B model required 64 A800 GPUs.
In GRPO training stage, our implementation is primarily based on modifications to the VerL frame- work [Sheng al., 2024]. Regarding specific parameter settings, we configure the training batch size to 64, with 16 roll-outs per data point and a maximum response length of 8, 8,192 tokens. Due to the significant distribution gap between Go task responses and the reference model's pretraining data, we set the KL coefficient (kl coef) to 5c-4. The 7B model training utilizes 32 A800 GPUs, while the 32B model reinforcement learning phase requires 64 GPUs.
B.2 Evaluation Details
For general benchmarks, all evaluations conducted using the OpenCompass framework. Specif- ically, the evaluation results for AIME and MATH datasets employ Qwenn..577BBIInstruu as the judge model. The performance metrics reported the BBEH benchmark represent the average performance across all the subsets.
C Further Discussions
C.1 Analysis on KataGo-Bench-IK
Since most of our experimental results on Go are evaluated based on the performance on the constructed KataGo-Bench-lI benchmark. it is essential to discuss the fairness of KataGo-Bench- IK. We select several models with different proficiency levels play against each other, and calculate the corresponding ELO ratings based on win-loss outcomes, thereby intuitively representing model capabilities through competitive relationships. The ELO rating is designed follows:
ELO(A) new FLOO())oi + K (SA FA)
Where:
ELO(A))ow is the updated Elo rating of player A after the match
ELO(AA) is the previous Elo rating of player A
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K is the weight coefficient that determines how much a single game will impact the rating (set 32)
SA is the actual of player A in the match (1 for win, 0.5 for draw, 0 for loss)
EA is the expected score of player A, calculated as:
E
+ 10(BLD1B)--L0)A))4400
Where (LO(B) the Elo rating of opponent B. The constant 400 determines how much rating difference corresponds to specific winning probability.
Based on this ELO rating system, we selected several models with varying performance levels and conducted a tournament to obtain the corresponding ELO ratings for each model. As shown in Figure 11, our experimental results demonstrate that the correlation coefficient between the models' ELO ratings and their performance on KataGo-Bent
IK is r = 0.92, indicating an extremely high correlation between these two metrics. Therefore, we can conclude that KataGo- Bieoo--BBh----- 1 K fair benchmark that accurately reflects the actual competitive capabilities of the models.
0.92
2000
ELO Rating
1200
KataGo-Bench-11 Performance (%)
Figure 11: Correlation between ELO rating and BeneGo--eenc--ee 1K performance.
Figure 12: Winrate curve when context curse occurs
C.2 Context Curse: A Case study
In Sec. 4.3, we introduce the phenomenon of declining LLM performance as game state length increases. Here, will study this phenomenon and its underlying causes through several games between LoGos and Golaxy-GiamtElephan during gameplay. The blue line represents Golaxy- Gaant---iantllehann's win rate, while the yellow line indicates Golaxy-Giantn GianleeaaaanElesaaat'' lead in points. As observed, LoGos maintained a significant advantage during the first 160 moves. However, within the subsequent 40 moves. following several mistakes by LoGos, the game situation completely reversed, ultimately resulting in LoGos's 's defeat.
Fig. 12 illustrates the win rate and score progression
Examining one specific mistake, the board position is shown in Fig. 13. At this juncture, the green positions represent the top-1 choice annotated by KataGo, whereas the red brown positions indicates LoGos's 's actual choice, which are incorrect. Black's previous move was position H8.
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Figure 13: Game state when error occurs
LoGos's prediction included the reasoning: "We need respond Black's challenge at position H8." In Go, responding to an opponent's attack in the local area is a common strategy, making this pattern easily learnable from the training data. However, the superior move H16 requires comprehensive value judgment. While such a decision is not particularly difficult for top amateur human players, it presents a significant challenge for LLMs due to their reliance on serialized input, making global pattern recogmition more difficult. This obstacle is what we name as the "context curse" faced by LLMs Go-related tasks.
M
17
17
16
14
19
19
12
Figure 14: Another game state, LoGos chose respond in the local area again
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C.3 Response Quality: More Case Studies
Instructions Given to Volunteers We invite more than twenty amateur Go enthusiasts provide manual evaluations and ratings based on game states and mooel--generaed responses. Our evaluation process is completely anonymous and randomly distributed, and the instructions do not have any privacy risks. The instructions are designed as follows:
Given game atate accuracy of the model-generated respense. You need consider both the correctaess of the itself and the accuracy of the correeponding explemation. The nove should be categorized as correct/incorrect, while the explanation should be classified into three categories: incorrect, or anbiguous. If you find it difficult to datermine the accuracy the current response's explanation, please
response generated by our LIN, please evaluate
anbiguoua category provide your reasoning.
Figure 15: Instructions given to volunteers
Case Studies on Successful Examples In this part, we present several examples where the model predictions are accurate. During our experiments and annotation process, the model outputs were in Chinese. Here, provide the translated version to facilitate understanding.
16
16
15
12
12
11
Figure 16: Case
Figure 17: Case
First, Figure 16 illustrates a correct prediction made by the model at the beginning the game. For this particular game state, the generated response is demonstrated in Fig. 18.
In the generated output, LoGos correctly predicts the next move position (D4) while accurately employing proper terminology: identifying D4 as star point on the board. Furthermore, LoGos successfully conduct accurate variation analysis and result summarization, selecting D4 as the correct next move from among several possible variations. It also appropriately applies the Go principle of "enter slowly" to support its reasoning the next move selection.
Figure 17 demonstrates another accurate prediction during the opening phase. This game state is sampled from professional player's match. In this position, the model correctly identifies R8 the optimal next move. The corresponding response is shown in Fig. 19. Within this response, the model again conducted comprehensive reasoning and variation analysis, selecting the correct next move through a natural and vivid presentation.
Next, present several mid-game game states and their corresponding responses. In typical Go match, the period from approximately move 75 to move 200 characterized by more intense confrontations and battles between the opponents.
As shown in Figures 20 and 21, these examples illustrate mid-game positions. In such situations, the model receives input sequences ranging from 75 to 150 moves in length, requiring to model complex

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Aha, Black's first nove ls Q16 at the atar point, shich is a very solid opening. As White, ve multiple response options. Let's analyze carefully.
In modern Go, facing star peint opening, White has three classic standard respenses: diagonal star point, playing attaching at the corner. Let's examine the apecific variations:
occupying
small point
came side, or directly
1. White accupies the diagonal star point at D4 This is the mont traditional response, similar the "diagonal stepping" concept
Chinase military stratagy. If choose this variation: 2.0-D4 3.X-Q3 4.0-D17 5.X-C15 6.0-E16 7.X-R6 8.0-C12 According to snalysis, White's
variation 52.3%, which is quite balanced. This approach maintainz global flexibility, alloving for pivoting tovard different diractions as
White occupiea the lower-left 03 This choice is also ressonable, reflecting the Go proverb "corners firet, then sides." The varistion night proceed as: 2.0-D3 3.X-Q3 8.0-C4
sinilarly 52.3%, suphasizing development toward the lover-left direction. represente
5.X-05
approach
direct, aggressive appreach: 2.0-D16 3.x-q3 4.0-D3 6.X-C5 6.0-C4 7.X-D6 8.0-F3 9.X-C17 Although the vin rate remains 62.3%, this approach nore aggressive and night complicate the gane prenaturely. After careful consideration, I believe D4 is the optinal choice for three reasone: 1) It adheres to the fundamental Go principle of "cornere first, then sides"; 2) maintains global belance;
White chooses
providea nore possibilities Moreover, siace Black has only played one nove, there's no need to define the gane pattern too early. Choosing aligns patience while waiting for better opportunities.
subeequent developnent,
slovly," encouraging
player: White Bext position: D4 Win rate: 52.3%
Figure 18: Response demonstration for case
move, snall knight'a at P4, shows considerable insight-it both restricts Black's developneut around Q3 and devalopment (Buddenly sitting upright) Rovever, Black is not be underestinated! Let's analyze several key points carefully:
Hrm... position
interesting-
laya groundxork
lower right cornar.
BB particularly tempting. (Gesturing ovar the board) If Black can establish a foothold here, it not anly creates resonance knight's foruation. anticipate White
The contest for the lover right
opena gap White's night attempt an invasion at 04, but Black can respord with 14, usintaining initiative in the center.
Potential opportunity in the upper left corner: Q9 ie also worth considering. (Slightly shaking head) While it could construct an interesting shape, it's perhape passive and doesn't indeed indicates this isn't the optinal choice.
sufficient direct pressure Whita. The 46.6% vin rate
possibilities: (Quickly surveying
board)
pointe
conservative or... (suddenly laughing) frankly speaking, they hardly qualify as proper noves! Espacially this stage,
approach.
(Rolling black stope between fingers) After careful analyeis, the RB varistion proves nost advantageous: 21.X-R8 22.0-04 23.I-K4 24.0-H5 25.X-M4 (Striking the board decisivaly) This alloss Black establish solid bridgehead in the lovar right! Though the 47.4% vin rate doean't represent an overvheining advantage, the gane is still long, this choice
maintain initistive.
(Adsrensing an inaginary opponent) "White san, your P4 move was indeed elegant, but my Let's see who
equally
laugh lover right corner!"
Next player: Black
Bext position:
Win rate: 47.
Figure 19: Response demonstration for case 2
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A
19
75
15
15
14
14
13
Figure 20: Case 3
Figure 21: Case 4
Bum... Black's 69th nove at B11 is quite sharp. This nove has ne somewhat perplezed, eimultaneously threstens ny formstion the left development space in the center-truly a "good nove."
amalyze current position. The battle on the left side is critical; Black's B11 move clearly ains to gain khite,
(Sitting upright, carefully examining board)
initiative in
respond cautiously.
(Taking fan and gently waving it) I've considered the following response options: Option One: White B12 This nove direct and effective, eubodying the Go principle "attack while defending oneself. B12 stable stracture. Let's project the subsequent variations: 70.0-B12 71.X-B6 (Black night
with the previous B14, constructing
72.0-F5 (A solid response, simultaneously expanding toward the center) 73.X-E5 74.0-F4 75.X-04 variation, Whita's rate 58.9%, making
attempting
influence
76.0-E7 77.X-F8 (Nodding alightly) it favorable choice.
Option Tvo: White C11 (Frovning in contemplation) This nove also appeare promising, but may lack directness. While C11 offers flexibility, potentially leaves Black with mare opportanities: 70.0-C11 71.X-09 72.0-812 73.X-E6 74.0-F5 X-85 76.0-F4 77.X-04 (Shaking bead gently) less advantagoous.
variation,
54.8%, alightly
(Setting dovn the fan, crossing hands) Comparatively, B12 clearly prevails. It not only directly addresses Black's threat but also accomodates developuent in multiple directions, creating excellent synergy with B14. Should Black atteupt complications here, sufficient counterncasures.
(Biight smile) While the opponent's 'a B11 nove did cause sone perplexity, our 812 response pressuze
the principle "eater slouly when approaching eneny territory" ghile maintaining our advantage.
should
player: White
Bext position: B12
Win rate: 58.9%
Figure 22: Response demonstration for case 3
positional relationships. During this phase, even minor mistake at any moment can cause dramatic fluctuations in win probability. We present two cases where the model made correct predictions. Similar to Case 2, these game states are sampled from professional players' matches. According to human evaluation, these game states represent particularly critical phases of play. Figure 22 displays the
response for the third game state demo. Notably, the model correctly addresses challenge appearing on the left side, maintain local structure, and appropriately applies various Go terminology and principles. In the demonstrated game state, the move chosen by LoGos (B12) is the only correct solution. Additionally, the model demonstrated the ability to generate colloquial and natural responses.
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Case Studies on Misuse of Go Terms Next, we examine cases that illustrate the two issues mentioned in Section 4.2: the model's incorrcct usage specific terminology particular positions and the difficulty in evaluating the correctness of model-generated responses. First, Figure 23 shows a position from the opening stage. Here, the model correctly selects D17 as the predicted next move. However, the model erroneously describes D17 at DI6 one intersection away. While this error does not affect the accuracy of the model's final prediction, it objectively reflects discrepancies in the model's understanding of specific terminology.
"star point," which actually refers to the position
Similarly, in Figure 24, the model characterizes the relationship between the predicted next move F17 and the existing stone at D17 "knight's s move,' when the correct term should be "one-space jump"=anotin another instance of terminology misuse. Overall, the model's terminology errors appear predominantly during the opening stage, likely because the opening phase involves richer variety of shapes and associated terminology.
18
17
16
16
16
15
13
12
12
11
8
7
2
A
4
Figure 23: Case 5
Figure 24: Case
Lat us carafully analyza this opaning position. ...
**D17 pointes This is the most orthodox response. Choosing star point
Let us analyze the current position carefully. ... 1. F17 This is ny nost recomeended move. The elegance of Forning knight
upper left corner creates diagonal relationship with Black's stone, representing very classical opening strategy. The subsaquent variation night develop as follous:
this play liee with D17, building thickness
Figure 26: Response demonstration for case
Figure 25: Response demonstration for 5
Case Studies on Ambiguous Explanations In Section 4.2, we also discuss instances where LoGos provides responses whose correctness difficult to assess accurately. Below, we present corresponding example in Fig. 27. In this position, the model's prediction at O4 would typically be described as cutting Black's connection the lower right and punishing Black's overplay this area. However, the model characterizes the effect of this move as "not only neutralizing Black's ss offensive, but also establishing solid base for our side in the lower right." While this description is far from incorrect, from response quality perspective, it provides relatively limited effective information and does not fully align with human-cxpected intent. Therefore, responses this quality are considered ambiguous.
We consider the occurrence this phenomenon to be quite reasonable. During the GRPO training phase, we do not incorporate any reward related to terminology usage for the model. Indeed, adding such a reward is not straightforward task, as currently no language model possesses the capability to correctly understand Go board terminology, which implies that training corresponding reward model would be an entirely new challenge. Nevertheless, it surprising that despite not receiving any mamiioology-reltttee rewards during training, LoGos still maintains relatively high descriptive
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19
18
Let us analyze the position carefully: After careful deliberation, 04 is undouhtedly the optinal choice. not only neutralizes Black's offensive, also establishes solid base for our aide in the lower right. (Sitting down agein, confidently picking up etone) It's time to shov Black what true defensive counterattack looks lika!
13
13
12
Figure 28: Response demonstration for Case
A
Figure 27: Case 7
accuracy and successfully employs various specific terminologies acquired from the commentary dataset.
<Step1> (player identity deternination) The last move 18 141-X-J7. The next
player white.
<Step2> (analysis of several potential next noves) Try 142.0-C10, the subsequent possible variation sould ba: 142.0-C10
145.X-D10 146.0-E9 147.X-E10 148.0-F9 149.x-FII 150.0-G9 In this variation,
white's win rate 52.4%.
Try 142.0-D10, the subzequent possible variation would be: 142.0-D10 143. X-C10 144.0-C9 145.X-D9 146.0-E9 147.X-E10 148.0-F9 X49.X-F10 150.0-69 In variation, white's Try 142.0-09, the subsequent possible variation would be: 142.0-C9 143 -4-1X 145.X-D9
148.0-F9 149.I-F10 150.0-09 this
variation, white's vin rate 51.5%. <Step3> (eumnary) Considering all the above analysie, the best move is 142.0-C10. This leads alternatives. The nove at C10 effectively controls the left side of the mintaining <Stop4> (structured output.)
52.4%, which higher than the
position overall.
player:
Next position: C10
rate:
Figure 29: Examples of the next step prediction dataset. The heuristic template consists of four parts: (i) confirming whether the next player black white; (ii) analyzing several possible next moves; (iii) summarizing and selecting the optimal next move; and (iv) structured output.
D More Demonstrations of Prompts and Datasets
D.1
Next Step Prediction Dataset
In Fig. 29, we present a complete response constructed based on heuristic rules from the Next Step Prediction Dataset.
D.2
Commentary Dataset
In Fig. 30, we show a case from the Go Commentary Dataset. The comments are sourced from professional and top amateur Go players' game analyses. We collect and process the data to establish
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Jump point ceater.
In case, due to the unfavorable position, Black is now weeking to eaploy extraordimary measures.
Figure 30: Example of the Go commentary dataset
one-to-one correspondences between game states and comments, and apply rule-based filtering to remove low-relevance comments (such as greetings the beginning of games or announcements of winners at the end). Through this process, we construct Go Commentary Dataset comprising 100K entries.
D.3 System Prompt in GRPO
During the GRPO training phase, we employed system prompt entirely different from that used in the cold start phase, aimed at encouraging the model to think and predict through reasoning paradigm. Our system prompt is shown in Figure 31.
is anslyze given gane record, assess the position, select several possible next noves for analycis, project subsoquent variations, and finally sumarizze and select the optimal next nove. In the provided gane, "I" represents Black stones, and "g" represents White stanes. The board coordinate expressed
professional player.
reasonable analysis
reflection,
19x19, with number. Letters range from A-T (skipping 1), corresponding to positions right on positions from botton top.
folloued
Numbers range 1-19, corresponding
analysis reflection position, nake logical predictions, projections, and analysea of subsequent noves, and finally sumaarize your reasoning to select the nost appropriate Please provide rigoroua and detailed analytical reasoning, with tinely sumaaries. Your output fornat should be: creasoning?
current
Your thought process.
</reasoning?
<answer>
\boxed(Next playar: Black/ihita)
Mboxed(Next position: location)
\bozed(Win rate: percentage)
Figure 31: Example of the system prompt used in GRPO
E 2-D Board Rendering Demo
In this section, we briefly introduce the method we use to render a given move list to the 2-D board state.
For instance, for a move list:
1-X-D16 3.X-Q4 4.0-016 5.X-017 6.D-R14 8.0-D3 9.X-C4 10.0-D5 11.X-B6 12.0-R6
Figure 32: Example of original move list.
The rendered 2-D board state shown in Fig, 33. Where represents black, -1 represents white, and 0 represents an empty position.
More descriptions of Go
Here some simple explanations for better understanding of Go.
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to,
6,
to,
[0.
Figure 33: Rendered 2-D board state.
Go is a two-player game where one player uses black stones and moves first, while the other player white stones. The two players take turns placing stones.
In Go, each stone must be placed an intersection point. Each stone survives if and only if it has at least one "liberty" (breathing space). When a stone is surrounded by the opponent's stones on all adjacent sides, it considered to have zero liberties and is "captured". Typically, a game ends when all stones from both sides are alive, and further moves cannot guarantee survival. At this point, both players agree to end the game and count the total number of their surviving stones plus the enclosed intersection points. The player with the higher total wins. For experienced players, the game end simply by mutual agreement that all moves are complete and there is no more question about the life and death status of stones on the board, without needing continue placing stones.
"Capture" is professional Go term that refers to the action of placing a stone that removes all liberties from the opponent's 's stones, thereby clearing the captured stones from the board. This is one of the most fundamental rules of Go.
G Broader Impacts
In this paper, we do not utilize any non-public data. All our game records and commentary data are obtained from open resources the internet, containing only move information, comments, and necessary win-loss relationships, thus posing no privacy risks or negative societal impacts.
From positive perspective, propose strong method for integrating domain-specift, expert-level knowledge with applications in the Al industry.
purpose LLMs, providing a novel approach for a wider range of LLM
30


Reviews:
[{"review_id": "0aq0XsaTiU", "reviewer": "Reviewer_vfsx", "summary": "This paper proposes a method for integrating domain-specific expert knowledge into general-purpose LLMs, using the game of Go as a case study. The authors introduce LoGos, a general LLM that achieves professional-level Go play while maintaining strong performance on general reasoning tasks like math and code. The key idea involves a two-phase process: (1) mixed cold start fine-tuning using a combination of domain-specific (Go) structured data and general CoT data; and (2) reinforcement learning via GRPO using a custom reward function designed around move quality and win rate estimation. The authors also construct a large-scale Go dataset and a new evaluation benchmark (KataGo-Bench-1K), releasing both to the community. Experimental results show that LoGos surpasses all existing general LLMs in Go performance and matches or exceeds strong amateur and professional Go models, while still performing competitively on standard general benchmarks.", "questions": "1. Could the authors clarify what specific aspects of the training pipeline are novel? How does this approach differ from existing fine-tuning, RLHF or domain-alignment methods?\n\n2. The paper posits that the proposed method can be extended to other specialized domains. Could the authors discuss concrete application areas where similar structured expert knowledge and heuristic rules could be constructed at scale? Are there any early results or experiments in these domains? \n\n3. Does the Go domain offer any general insights that can inform applications in other fields?", "limitations": "Yes.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n\n1. The paper tackles an interesting challenge - how to extend LLMs to perform effectively in domains with limited natural language corpora but rich structured expert data. Go serves as a compelling testbed with clear evaluation metrics.\n\n2. The methodology is sound and rigorously evaluated. Dataset construction and training details are clearly documented.\n\n**Weaknesses:**\n\n1. While the mixed fine-tuning with structured Go expertise and general long Chain-of-Thought (CoT) reasoning data as a cold start, followed by reinforcement learning is well-executed, the overall training strategy closely resembles existing practices in LLMs.\n\n2. The authors claim that the method can generalize to other specialized domains, yet the paper lacks concrete examples or preliminary results beyond the Go domain. While the approach is demonstrated effectively on Go, the feasibility of applying this method to other specialized domains remains speculative. The reliance on structured expert data (e.g., KataGo) and hand-crafted heuristic rules may limit scalability to less formalized domains.", "was_revised": true, "final_justification": "After carefully reading the entire discussion between the author and all reviewers, I appreciate the author's effort and have accordingly increased both the quality and originality scores. However, my opinion and suggestions remain the same as those of Reviewer Dp9f, so I am keeping the overall rating unchanged and look forward to more details (e.g., dataset, training parameters, model weights, etc.).\n\nOne minor concern is that, to the best of my knowledge, AI for Science research suffers from a lack of real-world data. This is why distillation and synthetic data are often used, rather than professional data used in this paper. Therefore, the generalizability of the proposed method remains in doubt.", "rebuttal": "Thank you for your valuable comments! We sincerely appreciate your review, detailed comments, and valuable suggestions, and are pleased to address your concerns and provide the following clarifications. We will clarify the questions and the weaknesses in order, and we sincerely hope our responses can address your concerns.\n\n## **Question 1: Could the authors clarify what specific aspects of the training pipeline are novel? How does this approach differ from existing fine-tuning, RLHF or domain-alignment methods?**\n\nThank you for your insightful question! Indeed, our method design draws inspiration from works such as DeepSeek-R1, which enhance model reasoning performance through a combination of cold start and RL. However, we must emphasize the significant differences between our work and these prior approaches. \n\nIn many existing works, the cold start phase **relies on distilled data** from stronger reasoning LLMs (e.g., Fin-R1[1], Med-R1[2], ReTool[3], Table-R1[4], Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start[5], etc.). For tasks like Go, however, general LLMs perform poorly, making the idea of using stronger model distillation for cold start **infeasible**.\n\nTo address this, we propose a novel cold start + RL approach. Instead of distilling from strong LLMs, we construct expert-level Go dataset based on heuristic rules only **(which is scalable and does not require a generative model)**, and mix it with a long COT general reasoning dataset for cold start training. Next, by adjusting the instructions, we **guide the model to perform Go tasks in the form of long COT reasoning** during the GRPO phase, successfully transferring the Go expertise learned in the cold start stage to the long COT reasoning paradigm.\n\nThus, our propose method **combines the reasoning capabilities of large language models on general tasks with structured, non-natural language expert knowledge from out-of-distribution (OOD) tasks**, ultimately enabling general LLMs to perform high-quality natural language reasoning on specialized tasks. During the RL phase, we combine the ***reasoning ability*** learned from general reasoning data with the ***Go knowledge*** acquired from specialized Go dataset, and successfully transfer the expert knowledge into the long COT reasoning paradigm. Compared to tasks that can directly use distillation datasets or high quality natural language corpora for cold start, our method **both provides and proves the effectiveness of a novel practice for specialized domains like Go**, where NL corpora are scarce.\n\nIn short, our method differs from existing training paradigms in terms of the **problem being solved**, **the data systhesizing approach for cold-start**, **the purpose of RL**, and the **specific experimental design of RL**. Therefore, our method possesses its own novelty. We hope the above clarification addresses your concerns!\n\n## **Question 2:  Could the authors discuss concrete application areas where similar structured expert knowledge and heuristic rules could be constructed at scale? Are there any early results or experiments in these domains?**\n\nThank you for this meaningful question. We are willing to discuss related domains to provide insights for future work. In fact, we believe that as long as the environment can **provide timely and correct feedback**, and there exists methods to **construct domain-specific data at scale**, our approach can be applied to enable general LLMs to learn reasoning tasks in specialized fields.\n\nHere is a possible idea: taking **circuit design** as an example, suppose we already have a large amount of simulation data(which indeed is easy to acquire at scale). We can then construct a domain-specific SFT dataset for circuit knowledge. By mixing such a specialized dataset with a general-domain long COT reasoning dataset for a mixed cold start. Similar to our practice on Go, we can use reasoning-oriented instructions in circuit design tasks, encouraging the model to output both reasoning processes and design results(**which can then be automatically verified for correctness in a simulation environment**). In this way, we can obtain a general model capable of both reasoning and correctly performing circuit design tasks.\n\nThe core of this problem is that, for a specialized domain, large-scale data is often **structured, standardized, and non-natural language**. Our method focuses on **combining** such structured domain knowledge with the reasoning abilities acquired by the model on general reasoning tasks, enabling the model to transfer knowledge learned from specialized data into natural reasoning thoughts. Therefore, as long as a specialized task has a **sufficiently large structured dataset** and **an environment for validation**, our approach is a promising direction to consider. Given that we have already validated the effectiveness of this method on the challenging task of Go, we believe it also has potential for application in more specialized domains.\n\nFor the second question (early results or experiments in these domains), we are currently conducting research in scientific areas, and experiments are still in the design phase.\n\nWe hope the above clarifications address your concerns!\n\n## **Question 3: Does the Go domain offer any general insights that can inform applications in other fields?**\n\nThank you for this valuable question! In fact, our choice of the Go domain for experiments is **carefully considered**. First, the next-move prediction task in Go is a challenging and specialized task, with the corresponding natural language corpus for this task is essentially **non-existent** on the Internet. Therefore, Go is almost completely an **out-of-distribution (OOD) task** for both open-source and closed-source general LLMs, and can thus be viewed as a professional task requiring extensive domain expertise. Given this premise, we believe that if general-purpose reasoning models can achieve professional-level performance on the Go task, then reaching human expert levels on similar professional tasks **with scarce natural language corpus** is also a possible future achievement.\n\nMeanwhile, the Go task already has powerful policy models such as KataGo and AlphaGo, making it feasible to **obtain scalable structured domain knowledge**. Alse, existing AI models can serve as reference benchmarks for comparing model performance, allowing us to conveniently verify the performance level achieved by our models. \n\nBased on this foundation, we successfully demonstrate that our model achieves professional-level Go performance while maintaining outstanding general-purpose capabilities, which fully proves the effectiveness of our approach. Therefore, our task **provides a potential solution for other similar professional tasks with comparable characteristics (poor LLM performance, scarce natural language corpus, but scalable structured domain knowledge available)**, namely leveraging the natural generalization of reasoning paradigms in reasoning models on OOD tasks, and transferring structured domain knowledge into long CoT reasoning outputs through self-exploration.\n\n## **Weakness 1:**\nPlease refer to our clarification to question 1.\n\n## **Weakness 2:**\nPlease refer to our clarification to question 2 and 3.\n\n## **Summary**\n\nFinally, thank you once again for your time and effort during the review process! We hope that these clarifications address your concerns about the paper, and we look forward to engaging in further discussions with you during the Discussion phase!\n\n## **References**\n[1] Liu Z, Guo X, Lou F, et al. Fin-r1: A large language model for financial reasoning through reinforcement learning[J]. arXiv preprint arXiv:2503.16252, 2025.\n\n[2] Lai Y, Zhong J, Li M, et al. Med-r1: Reinforcement learning for generalizable medical reasoning in vision-language models[J]. arXiv preprint arXiv:2503.13939, 2025.\n\n[3] Feng J, Huang S, Qu X, et al. Retool: Reinforcement learning for strategic tool use in llms[J]. arXiv preprint arXiv:2504.11536, 2025.\n\n[4] Yang Z, Chen L, Cohan A, et al. Table-r1: Inference-time scaling for table reasoning[J]. arXiv preprint arXiv:2505.23621, 2025.\n\n[5] Wei L, Li Y, Zheng K, et al. Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start[J]. arXiv preprint arXiv:2505.22334, 2025."}, {"review_id": "darLWDxF1Y", "reviewer": "Reviewer_eZ1d", "summary": "This paper studies how to incorporate domain-specific knowledge in Go into a large language model while preserving general reasoning capabilities. The authors collected a large-scale next-move prediction Go dataset, formatted with a heuristically generated template, and a moderate-scale commentary is also processed to be part of the training data (give commentary with terminologies in natural language). For training general-purpose LLMs to equip Go knowledge, the authors further propose a two-phrase pipeline (also widely seen in recent RL for LLM reasoning papers) including a mixed cold start fine-tuning and reinforcement learning (GRPO) with KataGo annotations as reward signals.\n\nComprehensive experiments were made to evaluate both the Go-specific performance and general reasoning competence of the two-phrase trained models. Trained models (with Qwen as base) can attain professional-level Go capabilities and preserve general mathematical reasoning performance (for base models, it can even boost mathematical reasoning).", "questions": "What are the Elo scores of the models? What is the performance of Mixed Cold Start (without RL)?", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "#### Strengths\n- The paper is well-written and enjoyable to read. In general, it is interesting to see how general-purpose large language models can be turned into professional-level Go models with some curated data. It's an interesting topic and I think Go itself is a good domain for testing these RL bootstrap reasoning methods.\n\n- The collected next-move prediction + commentary dataset is good, and I acknowledge the authors' effort made on this. It would be a good resource for the community to have easy access to Go-specific knowledge in a natural language format.\n\n- Extensive experiments show the effectiveness of the proposed method. It's also good to see how general reasoning performance is preserved. It is also a bit surprising to see that being trained on Go dataset can lead to improvements on mathematical reasoning tasks (even outperforming classic instruction fine-tuning in some cases). \n\n#### Weaknesses\n- Limited evaluations. For evaluating Go-specific performance, this paper uses the self-proposed KataGo-Bench. Although there is a correlation analysis between Elo rating and KataGo-Bench, I'm still curious about the Elo score of the trained models. In my memory (correct me if I'm wrong), GPT-3.5/4 already has an Elo score of 1700+ (which is about 85 on KataGo-Bench in Figure 9), But why did closed-source models in Table 1 perform so poorly? \n\n- Limited methodological contributions. It is not surprising that cold-start + GRPO can be applied to LLMs to boost domain-specific reasoning capabilities like Go. I see the main contribution of this paper as the KataGo-Bench and the Expert-level go dataset. The two-phase RL-style training is more like a baseline evaluation of the effectiveness of the proposed dataset rather than a major contribution itself.", "was_revised": true, "final_justification": "After reading the authors' response, I have decided to increase my rating by one. Although there might be limited technical contributions, I think this paper might be of interest to a general audience at NeurIPS, so I recommend weak acceptance.", "rebuttal": "Thank you for your insightful comments! We sincerely appreciate your review, detailed comments, and valuable suggestions, and are pleased to address your concerns and provide the following clarifications. We will clarify the weaknesses and the questions in order, and we sincerely hope our responses can address your concerns.\n\n## **Weakness 1: Question about Elo score (closed-source models' performance)**\n\nThank you for this thoughtful question! Regarding the question about GPT-3.5/4 Elo scores, we must clarify that the \"1700+ Elo score\" was achieved on **Chess**, **NOT on Go** (Due to official requirements, we cannot provide links, but this information is publicly available through web search).\n\nIn our Go gameplay testing, all well-known general-purpose large models (including GPT4, o1, DeepSeek-R1, Gemini, Claude, Doubao, Qwen-Max) perform **far below even beginner level**, with performance essentially equivalent to **randomly making moves on the board**. Therefore, we believe calculating Elo scores for these models is meaningless, since they cannot even finish a game without violating basic rules (like making invalid moves). \n\n**As of the time we write this rebuttal** (July 25, 2025), we can confidently assert that, all known general-purpose large language models, whether closed-source or open-source, **fall far short of Go beginner level** (i.e., the performance of KataGo-HumanSL-18K). Our model is **the first and only general large language model** to achieve professional-level performance in the Go domain.\n\nWe sincerely hope that the above clarifications address your concern!\n\n## **Weakness 2: The two-phase RL-style training (cold-start+RL) is more like a baseline evaluation of the effectiveness of the proposed dataset rather than a major contribution itself.**\n\nThank you for your valuable question! However, we **beg to differ** that your perspective on our method (limited methodological contributions) has certain limitations. While cold-start + GRPO has indeed become a common practice for reasoning enhancement of LLMs in general domains, most existing works still rely on **distillation data from stronger reasoning models** for their cold-start methods. For example, Fin-R1[1], Med-R1[2], Retool[3], Table-R1[4], Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start[5], etc. \n\nHowever, for the Go domain, since even the strongest reasoning models cannot obtain correct reasoning paths, the approach of using stronger reasoning models for distillation has completely failed.\n\nTo address this, we propose a novel cold start + RL approach. Instead of distilling from strong LLMs, we construct expert-level Go dataset based on heuristic rules **(which is scalable and does not require a generative model)**, and mix it with a long COT general reasoning dataset for cold start training. Next, by adjusting the instructions, we **guide the model to perform Go tasks in the form of long COT reasoning** during the GRPO phase, successfully transferring the Go expertise learned in the cold start stage to the long COT reasoning paradigm.\n\nThus, we propose a training method that **combines the reasoning capabilities of large language models on general tasks with structured, non-natural language expert knowledge from out-of-distribution (OOD) tasks**, ultimately enabling general LLMs to perform high-quality natural language reasoning on specialized tasks. During the RL phase, we combine the reasoning ability learned from general reasoning data with the Go knowledge acquired from specialized Go dataset, and successfully transfer the expert knowledge into the long COT reasoning paradigm. Compared to tasks that can directly use distillation datasets or high quality natural language corpora for cold start, our method **both provides and proves the effectiveness of a novel practice for specialized domains like Go**, where NL corpora are scarce.\n\nIn short, our method differs from existing training paradigms in terms of the **problem being solved**, **the data systhesizing approach for cold-start**, **the purpose of RL**, and the **specific experimental design of RL**. Therefore, our method possesses its own novelty and contributions.\n\nWe sincerely hope the above clarification addresses your concerns.\n\n## **Question 1: What are the Elo scores of the models?**\nPlease refer to our clarification to weakness 1.\n\n## **Question 2: What is the performance of Mixed Cold Start (without RL)?**\nThank you for this rigorous question. In Figure 6(a), we demonstrate the performance change curve of the model during the GRPO process. At step 0, i.e., before training begins, this value represents the performance immediately after cold-start, where the 7B model achieves **62.63%** and the 32B model achieves **68.18%**. As a comparison, the KataGo-HumanSL-18K model, representing beginner-level performance, performs **67.4%**. This proves that after cold-start alone, the initial point cannot yet perfectly transfer the knowledge learned from the Expert-Level Go Dataset to long CoT paradigm reasoning.\n\n## **Summary**\n\nFinally, thank you once again for your time and effort during the review process! We hope that these clarifications address your concerns about the paper, and we look forward to engaging in further discussions with you during the Discussion phase!\n\n## **References**\n\n[1] Liu Z, Guo X, Lou F, et al. Fin-r1: A large language model for financial reasoning through reinforcement learning[J]. arXiv preprint arXiv:2503.16252, 2025.\n\n[2] Lai Y, Zhong J, Li M, et al. Med-r1: Reinforcement learning for generalizable medical reasoning in vision-language models[J]. arXiv preprint arXiv:2503.13939, 2025.\n\n[3] Feng J, Huang S, Qu X, et al. Retool: Reinforcement learning for strategic tool use in llms[J]. arXiv preprint arXiv:2504.11536, 2025.\n\n[4] Yang Z, Chen L, Cohan A, et al. Table-r1: Inference-time scaling for table reasoning[J]. arXiv preprint arXiv:2505.23621, 2025.\n\n[5] Wei L, Li Y, Zheng K, et al. Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start[J]. arXiv preprint arXiv:2505.22334, 2025."}, {"review_id": "5UuX3zfyak", "reviewer": "Reviewer_AjQt", "summary": "This paper introduces LoGos, a large language model (LLM) that is specialized in playing the game of Go. The model is trained on a novel dataset that has been constructed based on real, annotated game states and Go commentary. A novel training paradigm, using the aforementioned dataset and chain-of-thought (CoT) reasoning data from a variety of domains for fine-tuning, is applied. Reinforcement learning is applied to improve the model’s next-move prediction performance, using a reward function that encourages the model to output the correct next move, given a game state, alongside the win rate estimation for the predicted move. LoGos outperforms other LLMs and a competitive AI system, KataGo, in accuracy on predicting the next move given a game state, while also maintaining general capabilities on math, reasoning, and coding benchmarks. According to human evaluators, LoGos produces accurate move predictions 96.5% of the time, but the explanations given in natural language are correct only 55.6% of the time.", "questions": "- Do the authors have suggestions for future work that could address the ‘context curse’ issue? \n- The proposed benchmark includes a measure of accuracy for next-move prediction, given game state, but is there a way to assess LoGos’ overall win rate in complete games? \n- Can the authors provide a reference for: \"Go contains more than 600 terms\" (p. 8)?", "limitations": "The aforementioned concerns regarding the data sourcing should be addressed.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\nIntegrating natural language explanations into Go-playing AI systems could be very a valuable entertainment and educational resource for the Go-playing community. The proposed model achieves admirable performance on a benchmark constructed to measure the accuracy of next-step prediction, given a game state. This paper represents a promising direction in Go AI research. \n\nWeaknesses:\nPerformance concerns: The following is acknowledged in the paper, but remains a considerable concern for the utility of the proposed model. Over the course of a game of Go, the context contained in the LLM input becomes increasingly long, and performance degrades. Additionally, the natural language explanations, which could be a core highlight of the proposed system, have accuracy issues. \n\nExposition: The description of the game of Go in Section 2.1 could be improved. There is not explicit information about the number of players, nor what constitutes a winning state for the game. It may also be helpful to explain basic mechanics of the game, like “capture”. The paper is not very accessible to people who don’t already know how to play Go. \n\nReproducibility concerns: There is insufficient information about the source of the Go commentary data. It would be helpful to have information about the properties of the data, e.g. average length of comments, language(s) included, and whether the commentary is automatically transcribed from video/audio data or inherently text-based. This data is crucial to the approach presented, so it seems important for the audience to be able to assess its provenance and quality. Additionally, there are potential ethical issues: even though the data is ostensibly ‘public,’ do the authors have permission to collect and redistribute it?", "was_revised": true, "final_justification": "The authors have addressed many of the initial concerns I had about the paper. I raised my score from 4 to 5.", "rebuttal": "Thank you for your valuable comments! We sincerely appreciate your review, detailed comments, and valuable suggestions, and are pleased to address your concerns and provide the following clarifications. We will clarify the questions and the weaknesses in order, and we sincerely hope our responses can address your concerns.\n\n## **Question 1: Do the authors have suggestions for future work that could address the 'context curse' issue?**\n\nThank you for your insightful question! In fact, during the period after submission, we conducted extensive experiments to address this issue and ultimately **found a quite effective solution**. Since Go is actually a game played on a 2D plane, we believe it is necessary for the model to **see the actual state of the board**. Therefore, while providing the move list to the model, we also render the Go board as a **19×19 2D array** and provide it to models in the query. \n\nIn this 2D array, **1 represents black, -1 represents white, and 0 represents positions where no stones have been placed yet**. Our rendering strategy complies with all Go rules (such as the rule of removing stones from the board). \n\nFor example, given the following move list:\n```\n1.X-D16 2.O-D4 3.X-Q4 4.O-Q16 5.X-O17 6.O-R14 7.X-C3 8.O-D3 9.X-C4 10.O-D5 11.X-B6 12.O-R6\n```\n\nThe rendered 2D array is:\n```\n[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0],\n [0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n [0, 0, 1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n```\n\nUnder the new setting, the model's input query contains **both move list information and actual board state information**.\n\nAfter following the same experimental procedure (cold start + RL), we are excited to see that using the new query, the model's 'context curse' problem is greatly alleviated. The table below shows the evaluation results on KataGo-Bench-1K:\n\n| Model | 1-50 | 51-100 | 101-150 | 151-200 | 200+ |\n|-------|------|--------|---------|---------|------|\n| **Former Models** | | | | | |\n| Original-RL_Step-1600 | 99.1 | 95.0 | 87.7 | 78.0 | 74.4 |\n| Original-RL_Step-1000 | 96.1 | 91.3 | 81.3 | 78.0 | 64.1 |\n| Original-RL_Step-500 | 86.1 | 75.2 | 68.0 | 59.3 | 33.3 |\n| **New Models** | | | | | |\n| Addboard-RL_Step-1400 | **97.3** | 94.3 | **91.1** | **92.3** | 89.7 |\n| Addboard-RL_Step-1200 | 96.1 | **94.6** | 89.2 | 85.7 | **92.3** |\n\nWe find that the models with updated input information demonstrates significantly **more accurate understanding of board states**, maintaining high prediction accuracy even under game sequences with 200+ moves. Therefore, we believe this new method effectively addresses the context curse challenge. We will supplement this interesting experimental result to the paper and provide corresponding discussions.\n\nWe hope the above clarifications address your concerns!\n\n## **Question 2: Is there a way to assess LoGos’ overall win rate in complete games?**\n\nThis is a very valuable question. From our perspective, the assessment of overall win rate in complete games can be **correlated with the game's final win rate**. In fact, to verify the fairness of the KataGo-Bench-1K evaluation set, we also sample several checkpoints for mutual gameplays and calculate the Elo scores of different checkpoints using the following algorithm. This part of the experiment is recorded in Appendix C.\n\nIn our designed Elo algorithm (following similar designs in [1-2]), for a certain player A, the score update formula is:\n\n$$\\text{Elo}(A) _ {\\text{new}} = \\text{Elo}(A) _ {\\text{old}} + K \\cdot (S _ A - E _ A)$$\n\nWhere:\n- $\\text{Elo}(A)_{\\text{new}}$ is the updated Elo rating of player A after the match\n- $\\text{Elo}(A)_{\\text{old}}$ is the previous Elo rating of player A  \n- $K$ is the weight coefficient that determines how much a single game will impact the rating (set to 32)\n- $S_A$ is the actual score of player A in the match (1 for a win, 0.5 for a draw, 0 for a loss)\n- $E_A$ is the expected score of player A, calculated as:\n\n$$E_A = \\frac{1}{1 + 10^{(\\text{Elo}(B) - \\text{Elo}(A))/400}}$$\n\nWe conduct pairwise matches with 50 games each, and calculated the Elo scores of various checkpoints (initial score of 1500) using the above formula. The table below shows the Elo scores and KataGo-Bench-1K scores of different checkpoints:\n\n| Model Name | Elo Score | KataGo-Bench-1K |\n|------------|-----------|-----------------|\n| GRPO_1600steps(32B) | 2139.25 | 89.6 |\n| GRPO_1500steps(32B) | 2130.93 | 88.6 |\n| KataGO-HumanSL-9d | 1895.64 | 87.8 |\n| GRPO_1000steps(32B) | 1851.83 | 85.0 |\n| GRPO_800steps(32B) | 1729.03 | 84.4 |\n| GRPO_200steps(7B) | 1515.56 | 78.3 |\n| GRPO_600step(32B) | 1513.23 | 83.7 |\n| GRPO_700steps(32B) | 1506.41 | 85.5 |\n| KataGO-HumanSL-7d | 1476.81 | 81.9 |\n| GRPO_900steps(7B) | 1435.78 | 86.7 |\n| GRPO_60steps(32B) | 1153.67 | 72.3 |\n| Initial point(32B) | 1040.59 | 68.18 |\n| KataGo-Human-SL-18k | 879.72 | 67.4 |\n\nWe believe that the Elo score here is **an indicator that can \"assess LoGos' overall win rate in complete games.\"**\n\nIt is also worth mentioning that we further calculated the correlation coefficient between the KataGo-Bench-1K scores and Elo, obtaining a result of **r=0.92**, which fully demonstrates the **high correlation between KataGo-Bench-1K and the actual game performance of the models**. Therefore, the performance on KataGo-Bench-1K can also well measure the overall win rate in complete games.\n\n## **Question 3: Can the authors provide a reference for: \"Go contains more than 600 terms\" (p. 8)?**\n\nThank you for this rigorous question. The statement about Go **having 600+ terms** comes from the *Chinese-English Dictionary of Weiqi Terms*, which contains 643 bilingual Chinese-English Go terms. We will supplement this information in the appendix.\n\n## **Weakness 1: The context contained in the LLM input becomes increasingly long, and performance degrades.**\n\nPlease refer to our clarification to question 1.\n\n## **Weakness 2: The description of the game of Go in Section 2.1 could be improved.**\n\nThank you for your thoughtful suggestion! Considering the page limit constraints, we only briefly introduce our modeling approach for the Go task in Section 2.1. We will add the necessary rule descriptions and the supplementary information you mentioned in the Appendix. Here we can provide some simple explanations for better understanding:\n\n1. **Go is a two-player game** where one player uses black stones and moves first, while the other player uses white stones. The two players take turns placing stones.\n\n2. **In Go, each stone must be placed on an intersection point**. Each stone survives if and only if it has at least one \"liberty\" (breathing space). When a stone is surrounded by the opponent's stones on all adjacent sides, it is considered to have **zero liberties** and is **\"captured\"**. Typically, a game ends when all stones from both sides are alive, and further moves cannot guarantee survival. At this point, both players agree to end the game and count the total number of their surviving stones plus the enclosed intersection points. The player with the higher total wins. For experienced players, the game can end simply by mutual agreement that all moves are complete and there is no more question about the life and death status of stones on the board, without needing to continue placing stones.\n\n3. **\"Capture\" is a professional Go term** that refers to the action of placing a stone that removes all liberties from the opponent's stones, thereby clearing the captured stones from the board. This is one of the most fundamental rules of Go.\n\nWe hope the above explanations help you better understand the rules of this fascinating game, and our work as well！\n\n## **Weakness 3: There is insufficient information about the source of the Go commentary data.**\n\nThis is a very helpful suggestion. Our commentary dataset consists primarily of Chinese language data, with lengths ranging from **10-50 tokens**. Our comments are not derived from video/audio data, **all obtained from publicly available or authorized Go game library files** in `SGF` format (a specialized format for storing Go game records). The game states being commented on are mostly from professional players' live games, with commentators being top amateur players or professional players. There are no ethical or privacy concerns about our commentary data. We will add this information to the appendix.\n\n## **Summary** \n\nFinally, thank you once again for your time and effort during the review process! We hope that these clarifications address your concerns about the paper, and we look forward to engaging in further discussions with you during the Discussion phase!\n\n## **References**\n\n[1] Zhang Y, Han X, Li H, et al. Complete Chess Games Enable LLM Become A Chess Master[J]. arXiv preprint arXiv:2501.17186, 2025.\n\n[2] Tian Y, Ma J, Gong Q, et al. Elf opengo: An analysis and open reimplementation of alphazero[C]//International conference on machine learning. PMLR, 2019: 6244-6253."}, {"review_id": "2cTKImi4vY", "reviewer": "Reviewer_Dp9f", "summary": "This paper presents LoGos, a large language model trained to achieve professional-level Go performance while maintaining general reasoning capabilities. The authors use a two-stage approach: (1) mixed fine-tuning with structured Go expertise data and general Chain-of-Thought reasoning data, followed by (2) reinforcement learning using GRPO. The model reportedly achieves 88.1% and 88.6%  accuracy on their KataGo-Bench-1K benchmark and wins against various Go AI models.", "questions": "As a Go game player, I genuinely like the topic of this paper. \n\nI have one major question: Can you provide game records and independent evaluation against established Go engines at known strength levels? The reason I'm asking is that I tried to quickly reproduce the strategies of using text encoding with commercial LLMs (Claude, OpenAI, etc.), yet I do not really observe consistent responses from LLMs. In particular, when I provide the sequential text encoding steps of Go games to LLMs, I do not see reasonable plays from the responses. The model more likely just hallucinates and generates a random response. I don't know if this is because of the issues of not fine-tuned on Go-related materisl, but I would like to see more evidence that the approaches proposed by the authors work. Therefore, it would be great if the authors could open-source the dataset, or at least provide some samples. \n\n1. Core Justification: Why use text encoding for Go when spatial representations are more natural? What advantages does this approach offer over existing methods?\n2. Understanding vs. Pattern Matching: How do you distinguish between the model learning Go principles versus memorizing patterns from training data?\n3. Conversation Logs: Can you share actual conversation histories showing the model's reasoning process during gameplay?\nSpatial Reasoning: How does the model handle complex spatial relationships (life/death, territorial evaluation) through text sequences?\n4. Generalization: How does performance vary across different board sizes or rule variants not seen in training?", "limitations": "- The text encoding approach may fundamentally limit spatial reasoning capabilities\n- Training methodology may overfit to evaluation metrics rather than genuine understanding\n- Limited analysis of failure modes and edge cases\n- Dependence on KataGo annotations may constrain learning to that engine's biases\n- Computational efficiency compared to specialized Go AIs is unclear", "rating": 3, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength: \n- Novel and ambitious application: Attempting to bridge domain expertise with general LLM capabilities is an interesting research direction\n- Comprehensive experimental design: The paper includes thorough ablation studies, multiple baselines, and evaluation across both Go-specific and general reasoning tasks\n- Transparent methodology: Detailed description of dataset construction, training procedures, and reward function design\n- Strong empirical claims: If validated, achieving professional-level Go performance while maintaining general capabilities would be significant\n\nLimitation:\n1. Fundamental Architectural Mismatch\nThe core premise is questionable: using sequential text encoding (\"1.X-Q16 2.O-D16...\") for a inherently spatial, visual game like Go. LLMs processing 150+ move sequences cannot realistically maintain spatial board understanding. This encoding forces the model to reconstruct 2D spatial relationships from 1D text, which is fundamentally inefficient.\n\n2. Insufficient Justification for Approach\nThe paper fails to adequately address why this text-based LLM approach is preferable to existing methods. AlphaGo and successors use direct board representations for good reason. The authors should justify why forcing spatial reasoning through text is beneficial.\n\n3. Questionable Performance Claims\n- The dramatic improvement from 60-70% to 88%+ through RL seems implausibly large\n- Win rates against specialized Go AIs need independent verification\n- The KataGo-Bench-1K benchmark is self-constructed and may not reflect true playing strength\n- Human evaluation shows only 55.6% correct explanations, suggesting pattern matching rather than understanding\n\n4. Limited Understanding Analysis\n- The paper lacks analysis of what the model actually \"understands\" about Go. The context curse analysis (Figure 8) shows severe performance degradation as games lengthen, suggesting the model doesn't maintain coherent board state understanding.\n\n5. Evaluation Concerns\n- Evaluation primarily on authors' own benchmark\n- Limited diversity in opponent strength assessment\n- No analysis of actual game quality or strategic understanding\n- Self-play evaluation would strengthen claims", "was_revised": true, "final_justification": "I decide to keep my scoring as discussed in the comments previously. In particular, my questions and concerns on reproducibility is not addressed.", "rebuttal": "Thank you for your valuable comments! It is exciting for us to communicate with a reviewer who understands both Go and our work, so we are very willing to provide clarifications for your concerns! We will clarify the questions and the weaknesses in order, and we sincerely hope our responses can address your concerns.\n\n## **Major Question: other LLMs hallucinate, need more evidence of our work**\n\nWe are very willing to provide such data to confidently demonstrate the impressive capabilities of our model! Since links are not allowed here, we choose to include `SGF` format game records and show the output of some step. We play LoGos against Golaxy (星阵) Giant Elephant (星巨象) model, which is a widely acknowledged pro-level model. As mentioned in our main text Figure 1, our model achieves a **55% win rate** against this professional Go model (in 60+ games).\n\n### Game Record 1(LoGos loses): \n\nSGF: \n```\n(;CA[utf8]AP[golaxy]PW[LoGos]PB[星巨象]WR[Unknown]BR[星阵1星]HA[0]RE[黑中盘胜]DT[25.07.26]KM[7.5]SZ[19]ST[0]RU[chinese];B[pd];W[dd];B[pq];W[dq];B[co];W[cp];B[do];W[fq];B[dk];W[qo];B[op];W[ql];B[cf];W[fc];B[cd];W[cc];B[bc];W[ce];B[bd];W[df];B[be];W[de];B[cg];W[nc];B[qf];W[pc];B[qc];W[qb];B[oc];W[pb];B[od];W[qd];B[rc];W[rd];B[rb];W[ra];B[ob];W[sb];B[kc];W[ic];B[qj];W[rq];B[kq];W[pi];B[pj];W[pg];B[pf];W[ni];B[oj];W[ng];B[qg];W[me];B[mc];W[ld];B[lc];W[je];B[eh];W[fj];B[ej];W[fh];B[fg];W[fi];B[eg];W[el];B[dl];W[gg];B[gf];W[hg];B[fe];W[cb];B[mk];W[lj];B[lk];W[kj];B[kk];W[jk];B[jl];W[il];B[jm];W[iq];B[rr];W[qq];B[qr];W[rk];B[rj];W[pn];B[rm];W[rl])\n```\n\nModel responses: \n- **Step 2:** \n```\n啊，黑棋第一手选择了Q16星位，一个非常经典开局。作为白棋，我们需要好好思考如何应对这个局面。\n\n(轻敲棋盘)让我来分析几种常见的应对方案：\n\n1. 白棋D16星位对角\n这是最经典的对角星布局，后续可能形成：\n2.O-D16\n3.X-Q3\n4.O-D3\n...\n这种变化下白棋胜率52.3%，相当均衡。这种对角星布局就像两位武林高手各自占据一个角落，各展其能。\n(content omitted due to space constraints)\n```\n\n- **Step 28:**\n```\n<reasoning>让我们仔细分析当前的局面。从全局来看，黑棋第27手R17的挡是必然的一手，既保护了自身利益，又对白棋Q17的小尖形成制约。现在轮到白棋，我们需要寻找既能扩张势力又能限制黑棋发展的好点。\n\n右上角的战斗尤为关键。白棋Q17的小尖已经对黑棋形成压力，此时若在R18的扳是绝佳的反击点：\n1. 直接对黑棋右上角施加压力\n2. 与白棋其他子力形成良好配合\n3. 为后续在右上区域的发展奠定基础\n4. 同时兼顾右下角的势力发展\n\n具体变化推演：\n28.O-R18的扳（妙手！）\n29.X-P17的打吃（黑棋的反击）\n30.O-Q18粘住（白棋加固自身）\n31.X-P16粘住（黑棋补断）\n32.O-P18粘住（白棋完全安定）\n33.X-O16长（黑棋的强手）\n34.O-N17跳（白棋轻松跳出）\n\n这个变化下白棋胜率53.3%，是一个积极的态势。相比之下，如果选择E9的小飞，虽然能扩张左边，但会放任黑棋在右上角加固，胜率仅50.9%，不够理想。\n\n特别值得一提的是，R18的扳具有\"一石三鸟\"的效果：\n- 限制黑棋扩张\n- 巩固自身阵地\n- 为后续作战创造支点\n\n对手如果应对不当，我们甚至可以在右上角形成\"铁壁\"般的厚势。不过要提醒自己\"不得贪胜\"，稳扎稳打才是取胜之道。\n</reasoning>\n<answer>\n{下一步颜色:白}\n{下一步位置:R18}\n{下一步胜率:53.3%}\n</answer>\n```\n\n- **Step 80:** \n```\n<reasoning>让我们仔细分析当前局面。这盘棋已经进行到第79手，黑棋刚刚在K7长了一手，这步棋很有意思啊。从整体格局来看，双方在四个角部都形成了星位布局，但战斗主要集中在中央和右侧。\n\n黑棋这手K7长显示了强烈的进攻意图。通常在这个阶段，黑棋会选择在J3拆边这样稳健的下法，但对手显然不甘于平淡。这手K7长让棋局变得更加复杂有趣了。\n\n现在轮到白棋的第80手，我认为有几个值得考虑的选择：\n\n1. J9扳（胜率49.7%）\n这步棋很直接，但似乎有些操之过急。变化可能是：\n80.O-J9扳（直接破坏黑棋形状）\n81.X-Q6点三线（黑棋依然会选择点三线）\n这个变化下白棋稍显被动，胜率49.7%，不太推荐。\n\n2. J3拆边（胜率50.5%）\n如果选择这个变化：\n80.O-J3拆边（拆边是经典的大场）\n81.X-Q6点三线（黑棋点三线，白棋拆边，形成经典对抗）\n82.O-R6挡（黑棋挡下是必然）\n83.X-J9扳（黑棋扳住，白棋在下方有发展空间）\n84.O-K10长（白棋长出，形成更稳固的形状）\n这个变化下，白棋能保持微弱的优势，胜率50.5%，棋很细啊。\n\n（摇头）其他选择如直接打入黑棋阵地，在当前局面下都显得不够明智。毕竟，黑棋在中央已经形成了一定势力，贸然深入可能会陷入被动。\n\n综合来看，J3拆边确实是当前的最佳选择。这步棋既符合\"入界宜缓\"的原则，又能为后续战斗创造有利条件。对手如果应对不当，我们还能获得更大优势呢！\n(content omitted due to space constraints)\n```\n\n### Game Record 2(LoGos wins): \n\nSGF (only provide part of the records due to space constraints): \n```\n(;CA[utf8]AP[golaxy]PW[LoGos]PB[星巨象]BR[星阵1星]HA[0]RE[W+R]DT[2025-07-2613:39:32]KM[7.5]SZ[19]ST[0]RU[chinese];B[pd];W[dp];B[pq];W[dd];B[qk];W[pp];B[qp];W[oq];B[op];W[po];B[nq];W[or];B[qq];W[nr];B[oo];W[qo];B[ro];W[rn];B[rp];W[on];B[mq];W[lq];B[mr];W[np];B[no];W[mp];B[nn];W[lr];B[om];W[qc];B[qd];W[pc];B[od];W[nb];B[cq];W[dq];B[cp];W[co];B[bo];W[bn];B[cn];W[do];B[bm];W[bp];B[an];W[bq];B[cf];W[em];B[fc];W[fd];B[gd];W[fe];B[dc];W[cc];B[ec];W[cd];B[dg];W[cj];B[fg];W[eh];B[eg];W[cl];B[be];W[ge])\n```\n\nNext, we are willing to discuss the **poor performance of other LLMs** you mentioned. We believe the reason is that the task of next step prediction is essentially non-existent in pre-training corpora, making it a completely **out-of-distribution task**. Therefore, general LLMs without specialized training can only hallucinate and randomly guess an output.\n\nAs a comparison, from the LoGos outputs provided, you can clearly see that although the model exhibits some hallucination in terminology and analysis, it does **perform reasoning, analysis, and prediction regarding the game situation.** This is significantly different from the random responses of all other LLMs. \n\nRegarding \"open-source the dataset, or at least provide some samples,\" we have already committed in our paper to **open-source all useful information**, including the complete training dataset, training methodology, model checkpoints, and evaluation benchmarks. We firmly believe our model represents **the first genuine step from AlphaGo toward human-understandable Go agents**, and we are looking forward to advancing this work.\n\n## **Question 1: Why use text encoding for Go when spatial representations are more natural?**\n\nThank you for this insightful question! After finishing this paper, we spent considerable time researching this issue and ultimately found an effective solution to the context curse problem - **a way to model 2D information through text.**\n\nFirst, we choose text encoding because we wanted to **elegantly perform Go task reasoning through general LLMs using natural language**. While spatial representation methods are effective, such methods deviate from the original intention of solving specialized problems through natural language. Such models ultimately **remain specialized models for single professional tasks** and cannot output human-understandable reasoning processes. Therefore, we choose to encode Go using text, attempting to combine the powerful general reasoning capabilities of LLMs with Go expertise.\n\nHowever, as you mentioned, using only move lists presented difficulties for the model's understanding of 2D board states. Through experimentation, we find a new approach: while providing move lists, we also **render the Go board as a 19x19 2D array**, in which 1 represents black stones, -1 represents white stones, and 0 represents empty positions. With the new input format, the model's \"context curse\" problem is greatly alleviated, achieving **90%+ accuracy in long games(200+ moves)**.\n\nDue to space constraints, please refer to our response to ***reviewer AjQt's Question 1*** for specific ***rendering details and experimental results***.\n\n## **Question 2: How do you distinguish between learning Go principles versus memorizing patterns from training data?**\n\nThis is a very interesting question. In fact, we believe the model definitely memorizes patterns from training data. However, since the number of possible variations (\\~250^150) is **significantly larger** than that of positions training data could contain (\\~10M), our model necessarily needs to generalize the learned patterns to unseen scenarios. Therefore, given that the model achieves professional-level Go performance, we believe it has **successfully generalized the learned patterns** and acquired correct Go principles.\n\n## **Question 3: Share actual conversation histories & How does the model handle complex spatial relationships?**\n\nThank you for your insightful question! In the **Major Question section**, we have already provided some conversation history. The provided responses show that LoGos can make judgments and reasoning based on the game situation quite well.\n\nAdditionally, here is an interesting example:\n```\nCN: 让我们仔细审视当前的局面。这盘棋已经进行到第192手，白棋刚刚在K15提劫，这已经是本局第7次在这个关键点提劫了。看来对手对这个劫争相当执着啊。\n\nTranlated vers: Let's carefully examine the current situation. This game has reached move 192, with White just taking the ko at K15, which is already the 7th time this point has been captured in this game. It seems the opponent is quite persistent about this ko fight.\n```\n\nHere the model correctly identifies from the move list that **a ko fight** is occurring at position K15, correctly counted the occurrences, and output such a description.\n\n## **Question 4: How does performance vary across different board sizes or rule variants not seen in training?**\n\nThank you for your helpful question! In fact, we conduct RL experiments **on 9x9 games without cold start** and find that the model still cannot directly learn Go strategies. Although we have not conducted complete cold start + RL process on 9x9 or other board sizes, we believe that since 19x19 task is proven to be learnable, reproducing this performance on smaller board sizes should be expected. \n\nAs for rules, we use Chinese rules, therefore when switching to other rules, the model's accuracy would shift, requiring additional fine-tuning.\n\n## **Weakness 1&2** \nPlease refer to Q1. \n\n## **Weakness 3**\n\n### W3.1 improvement seems implausible \nThis is an insightful question! We believe the RL stage is divided into two parts. First, after cold start, the model already achieves nearly 80% accuracy using **template prediction**, indicating that it has learned Go knowledge. Therefore, the first stage of RL is **transferring knowledge** learned from structured data to long COT paradigm reasoning. Next, after the model reaches parity with template prediction performance, it continues **self-exploration under reasoning paradigm**, ultimately improving to 88%+.\n\n### W3.2&W3.3&W3.4\nPlease refer to Major Question and Q1.\n\n## **Weakness 4**\nPlease refer to Major Question. We will add more model outputs to the appendix.\n\n## **Weakness 5: Evaluation Concerns**\n\nIn addition to the Elo analysis mentioned in Q1, we will add more analysis of model outputs in the appendix, including inviting more Go players to help us conduct human evaluation.\n\n## **Summary**\nFinally, thank you once again for your time and effort during the review process! We are very pleased to have a reviewer who is familiar with Go, and therefore we hope our clarifications can address your concerns. We are also very willing to engage in more discussions with you during the Discussion phase!"}]

Author rebuttal:

Meta-review: This paper presents LoGos, a general-purpose LLM adapted to the game of Go through a mixed cold-start strategy and reinforcement learning. While building on existing techniques, LoGos achieves professional-level Go performance while preserving general reasoning abilities—an interesting and potentially valuable result for the community.

Notably, the authors have committed to open-sourcing their dataset, models, and benchmark in the rebuttal, which would significantly enhance the impact and reproducibility of this work. Given the borderline scores, I believe acceptance is justified only under the condition that the authors fulfill this open-source commitment.

Therefore, I recommend acceptance contingent upon the public release of these resources as promised. I sincerely hope the authors follow through on this commitment to ensure the broader community can benefit from their work.
Decision: Accept (poster)