🎯 GameTheory-Solver

A QLoRA fine-tuned adapter for Qwen2.5-7B-Instruct, specialized in solving game theory problems with rigorous step-by-step mathematical reasoning.

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📋 Model Description

GameTheory-Solver is a LoRA adapter trained on the GameTheory-Bench dataset — the first comprehensive, computationally verified game theory dataset for LLM training. The adapter transforms Qwen2.5-7B-Instruct into a specialized solver that produces detailed, step-by-step solutions with mathematical proofs and clear final answers.

Key result: The fine-tuned model achieves 94% overall accuracy (up from 82% base) and 94.4% on hard problems (up from 66.7% base), representing a +12pp overall and +27.7pp hard-problem improvement.

🧠 Capabilities

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📊 Benchmark Results

Evaluated on a diverse benchmark spanning all 10 categories and 3 difficulty levels.

Overall Performance: Base vs. Solver

Per-Category Accuracy

Highlight: The model achieves the most dramatic gains on previously unsolvable categories — Bayesian Games (0% → 100%) and Mechanism Design (60% → 100%) — while maintaining perfect scores across zero-sum, sequential, and cooperative games.

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🚀 Usage

Installation

pip install transformers peft bitsandbytes accelerate torch

Loading the Model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

# Quantization config (matches training)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

# Load base model + adapter
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-7B-Instruct",
    quantization_config=bnb_config,
    device_map="auto",
)
model = PeftModel.from_pretrained(base_model, "Alogotron/GameTheory-Solver")
tokenizer = AutoTokenizer.from_pretrained("Alogotron/GameTheory-Solver")

Solving a Game Theory Problem

messages = [
    {
        "role": "system",
        "content": (
            "You are a game theory expert. Solve the given problem "
            "step-by-step, showing all mathematical reasoning. "
            "Provide the final answer clearly."
        ),
    },
    {
        "role": "user",
        "content": (
            "Consider the following game:\n\n"
            "Player 1 \\ Player 2 | Left | Right\n"
            "--- | --- | ---\n"
            "Up | (3,1) | (0,0)\n"
            "Down | (1,1) | (2,3)\n\n"
            "Find all Nash Equilibria."
        ),
    },
]

inputs = tokenizer.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

with torch.no_grad():
    outputs = model.generate(inputs, max_new_tokens=512, do_sample=False)

response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(response)

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🏋️ Training Details

Base Model

Dataset

QLoRA Configuration

Training Hyperparameters

Training Results

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⚠️ Limitations

• Small-matrix regression: Accuracy on 2×2 and 3×3 normal-form games decreased after fine-tuning (100% → 80% and 80% → 60% respectively). The base model already handled these well; the adapter slightly regresses on simpler subcategories while dramatically improving harder ones.
• Mixed-strategy precision: Complex mixed-strategy Nash Equilibria involving irrational numbers may have floating-point precision issues.
• Context length: Max sequence length of 2,048 tokens may truncate very large game matrices or extremely detailed solutions.
• Synthetic training data: The model was trained on programmatically generated problems; real-world game theory scenarios with ambiguous framing may require additional prompting.

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🔗 Links

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📚 Related Work

• "Game Theory Meets Large Language Models: A Systematic Survey" — IJCAI 2025 (arxiv:2502.09053) — The definitive survey on game theory × LLMs, covering RLHF alignment, multi-agent interactions, and strategic reasoning.
• DeepMind SHOR-PSRO (April 2026) — LLM-driven rewriting of game theory algorithms that outperformed hand-designed baselines (MarkTechPost).
• GT-HarmBench — Game-theoretic framing for AI safety benchmarking (arxiv:2602.12316).

📄 Citation

@model{alogotron_gametheory_solver_2026,
  author    = {Alogotron},
  title     = {GameTheory-Solver: QLoRA Fine-Tuned Game Theory Problem Solver},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/Alogotron/GameTheory-Solver},
  note      = {Phase 1 SFT adapter achieving 94\% accuracy on GameTheory-Bench}
}

📄 License

This adapter is released under the Apache 2.0 License.

📝 Citation

@misc{gametheory-solver-2025,
  title   = {GameTheory-Solver: QLoRA Fine-tuned Qwen2.5-7B for Game Theory},
  author  = {2reb},
  year    = {2025},
  publisher = {Hugging Face},
  url     = {https://huggingface.co/Alogotron/GameTheory-Solver}
}