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Alogotron
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Model Details
Model Provider
Alogotron
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
text
Qwen2.5-7B-Instruct (base) | +-- Phase 1: Supervised Fine-Tuning (QLoRA) | +-- GameTheory-Solver adapter | +-- Merged into: phase1_merged/ | +-- Phase 2: GRPO Reinforcement Learning +-- GameTheory-Reasoner adapter (this model) Trained on top of phase1_merged| Metric | Base (Qwen2.5-7B) | Solver (Phase 1) | Reasoner (Phase 2) |
|---|---|---|---|
| Exact Accuracy | 82.0% | 94.0% | 94.0% |
| Partial Accuracy | 82.0% | 94.0% | 94.0% |
| Format Quality | 0.92 | 0.70 | 0.70 |
| Reasoning Quality | 0.53 | 0.51 | 0.54 |
| Avg Response Length | 523 words | 169 words | 181 words |
| Difficulty | Base | Solver | Reasoner |
|---|---|---|---|
| Easy (n=9) | 100.0% | 88.9% | 88.9% |
| Medium (n=23) | 87.0% | 95.7% | 95.7% |
| Hard (n=18) | 66.7% | 94.4% | 94.4% |
| Category | Base | Solver | Reasoner |
|---|---|---|---|
| normal_form_2x2 | 100.0% | 80.0% | 80.0% |
| normal_form_3x3 | 80.0% | 60.0% | 60.0% |
| normal_form_3x4 | 100.0% | 100.0% | 100.0% |
| normal_form_4x4 | 100.0% | 100.0% | 100.0% |
| zero_sum | 100.0% | 100.0% | 100.0% |
| sequential_game | 100.0% | 100.0% | 100.0% |
| auction_theory | 80.0% | 100.0% | 100.0% |
| bayesian_game | 0.0% | 100.0% | 100.0% |
| cooperative_game | 100.0% | 100.0% | 100.0% |
| mechanism_design | 60.0% | 100.0% | 100.0% |
| Parameter | Value |
|---|---|
| Method | Group Relative Policy Optimization (GRPO) |
| Steps | 750 |
| Training Time | ~8 hours on RTX 3090 |
| LoRA Rank (r) | 32 |
| LoRA Alpha | 64 |
| Learning Rate | 5e-6 |
| KL Beta | 0.04 |
| Num Generations | 4 |
| Max Completion Length | 1024 |
| Reward | Range | Description |
|---|---|---|
| Accuracy | 0.85 to 1.0 | Verifies correctness against gold answers using domain-specific comparators |
| Format | 0.64 to 0.82 | Checks structured output format (think/answer tags) |
| Reasoning | 0.55 to 0.79 | Evaluates reasoning chain quality and mathematical notation |
| Total | 2.36 to 2.55 | Combined reward signal |
| Metric | Value |
|---|---|
| Final Loss | ~0.0002 |
| KL Divergence | 0.004 to 0.015 |
This adapter requires a two-step loading process since it was trained on top of the Phase 1 merged model:
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch # Step 1: Load the Phase 1 merged model as basebase_model = AutoModelForCausalLM.from_pretrained( "Alogotron/GameTheory-Solver", # or your local phase1_merged path torch_dtype=torch.bfloat16, device_map="auto",) # Step 2: Apply the GRPO Reasoner adaptermodel = PeftModel.from_pretrained(base_model, "Alogotron/GameTheory-Reasoner")model.eval() # Load tokenizertokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")python
system_prompt = ( "You are a game theory expert. Solve the following problem step by step. " "Show your reasoning clearly, then provide your final answer.") problem = "Consider a 2-player game with the following payoff matrix: " "L: (3,2) (1,4), R: (2,3) (4,1). Find all Nash Equilibria." messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": problem},] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): output = model.generate(**inputs, max_new_tokens=1024, do_sample=False) response = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(response)bibtex
@model{alogotron_gametheory_reasoner_2026, author = {Alogotron}, title = {GameTheory-Reasoner: GRPO-Trained Game Theory Reasoning Model}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/Alogotron/GameTheory-Reasoner}, note = {Phase 2 GRPO adapter with +6\% reasoning quality improvement}}Apache-2.0
Qwen2.5-7B-Instruct (base) | +-- Phase 1: Supervised Fine-Tuning (QLoRA) | +-- GameTheory-Solver adapter | +-- Merged into: phase1_merged/ | +-- Phase 2: GRPO Reinforcement Learning +-- GameTheory-Reasoner adapter (this model) Trained on top of phase1_mergedfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch # Step 1: Load the Phase 1 merged model as basebase_model = AutoModelForCausalLM.from_pretrained( "Alogotron/GameTheory-Solver", # or your local phase1_merged path torch_dtype=torch.bfloat16, device_map="auto",) # Step 2: Apply the GRPO Reasoner adaptermodel = PeftModel.from_pretrained(base_model, "Alogotron/GameTheory-Reasoner")model.eval() # Load tokenizertokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")system_prompt = ( "You are a game theory expert. Solve the following problem step by step. " "Show your reasoning clearly, then provide your final answer.") problem = "Consider a 2-player game with the following payoff matrix: " "L: (3,2) (1,4), R: (2,3) (4,1). Find all Nash Equilibria." messages = [ {"role": "system", "content": system_prompt}, {"role": "user", "content": problem},] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): output = model.generate(**inputs, max_new_tokens=1024, do_sample=False) response = tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(response)@model{alogotron_gametheory_reasoner_2026, author = {Alogotron}, title = {GameTheory-Reasoner: GRPO-Trained Game Theory Reasoning Model}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/Alogotron/GameTheory-Reasoner}, note = {Phase 2 GRPO adapter with +6\% reasoning quality improvement}}