ziadrone / airesupdated-v4

ARIES-RLVR v4: Experimental Training Checkpoint

Status: Experimental Checkpoint - This is a training checkpoint from batch 7, showing early reasoning capabilities.

🎯 Model Description

A Qwen3-4B model trained with Tree-of-Thoughts (ToT) reasoning methodology. This is an intermediate checkpoint (batch 7) during active training.

Training Progress

• Checkpoint : Batch 7/ongoing
• Training Method : Tree-of-Thoughts + Multi-agent policies
• Base : Qwen3-4B

🚀 Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "ziadrone/airesupdated-v4",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "ziadrone/airesupdated-v4",
    trust_remote_code=True
)

prompt = "What is 2 + 2?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🧪 Current Capabilities

Strengths

• Shows reasoning ability on multi-step problems
• Understands mathematical concepts
• Generates coherent explanations
• Can break down complex reasoning

Limitations

• Can be verbose (sometimes asks follow-up questions)
• May not always give direct answers
• Still under training - performance will improve
• Best suited for problems requiring step-by-step thinking

📊 Training Methodology

Core Components

• Tree-of-Thoughts : Multi-path reasoning exploration with adaptive depth
• Multi-Agent System : 4 specialized reasoning agents (Conservative, Exploratory, Balanced, Reflective)
• Adaptive Complexity : Automatic depth/branching adjustment based on problem difficulty
• Similarity Pruning : Removes redundant reasoning paths (90% threshold)
• Convergence Detection : Early stopping when paths reach same conclusion

Training Pipeline

1. Problem analysis and complexity estimation
2. Multi-branch reasoning generation with diverse strategies
3. Path evaluation and similarity-based pruning
4. Best path selection and answer extraction
5. Reward-based fine-tuning (RLVR-inspired)

⚠️ Important Notes

This is an experimental checkpoint from ongoing training:

• Not a final release
• Performance characteristics still being evaluated
• More training data and refinement planned
• Use for research/experimentation purposes
• Training may continue to improve this model

📈 Training Data

Multi-domain reasoning datasets:

• HuggingFaceH4/MATH-500 : Mathematical problem solving
• openbmb/RLPR-Train-Dataset : Reward-based reasoning patterns
• ai2lumos/lumos_multimodal_ground_iterative : Multi-step reasoning
• SAGI-1/reasoningData_200k : General reasoning tasks

🔄 Version History

• v1 : Initial experiment - basic ToT implementation
• v2 : Improved training pipeline with better pruning
• v3 : Enhanced reasoning quality, +12.5% accuracy improvement
• v4 : Current checkpoint (batch 7) - experimental, early training stage

💻 Technical Details

Model Specifications

• Parameters : ~4 billion
• Precision : BFloat16
• Context Length : Inherited from base (32K tokens)
• Architecture : Decoder-only transformer

Training Infrastructure

• Hardware : NVIDIA RTX A6000 (48GB VRAM)
• Framework : PyTorch + Transformers
• Batch Size : Adaptive with gradient accumulation
• Checkpointing : Every batch for recovery

🎓 Research Background

Implements techniques inspired by:

• ARIES (Autonomous Reasoning with Interactive Thought Graphs)
• RLVR (Reinforcement Learning with Verifiable Rewards)
• Tree-of-Thoughts : Systematic exploration of reasoning chains

📊 Example Output

Q: If 5 machines take 5 minutes to make 5 widgets, how long for 100 machines to make 100 widgets?

A: Let me think... Each machine makes 1 widget in 5 minutes. So 100 machines 
   make 100 widgets in 5 minutes. The answer is 5 minutes.

📄 License

Apache 2.0 - Same as base Qwen3-4B model

🙏 Acknowledgments

• Qwen Team at Alibaba Cloud for excellent base model
• ARIES and RLVR research teams for methodology inspiration
• Open-source reasoning datasets for training data

📧 Feedback

This is an experimental checkpoint. Feedback on reasoning quality and performance is welcome to guide continued training.

Note : This is a research checkpoint from active training. For production use, consider waiting for the final trained version or use the stable base Qwen3-4B model.