--- license: apache-2.0 base_model: Qwen/Qwen3-4B tags: - reasoning - tree-of-thoughts - qwen - rlvr - math - logic - reinforcement-learning pipeline_tag: text-generation language: - en --- # ARIES-RLVR: Advanced Reasoning Model A **Qwen3-4B** model enhanced with **Tree-of-Thoughts (ToT)** reasoning and **RLVR-inspired** training techniques for improved multi-step problem solving. ## 🎯 Overview This model extends the base Qwen3-4B with advanced reasoning capabilities through a custom training pipeline that combines tree-based exploration with reward-guided learning. The goal is to improve performance on complex reasoning tasks that require multi-step thinking and logical deduction. ## 🧠 Training Methodology ### Core Components #### 1. **Tree-of-Thoughts (ToT) Generation** - Multi-branch exploration of reasoning paths - Adaptive depth based on problem complexity - Parallel evaluation of alternative solutions - Best-path selection using learned heuristics #### 2. **Multi-Agent Policy System** Four specialized reasoning agents work in parallel: - **Conservative Agent**: Focuses on proven, reliable reasoning patterns - **Exploratory Agent**: Seeks novel solution approaches - **Balanced Agent**: Optimizes between exploration and exploitation - **Reflective Agent**: Validates and self-corrects reasoning chains #### 3. **Adaptive Complexity Scaling** - Automatically adjusts search depth (1-3 levels) based on problem difficulty - Dynamic branching factor (2-5 branches) - Early stopping when high-confidence solutions are found - Resource allocation proportional to problem complexity #### 4. **Hybrid Reward System** Multi-objective reward function combining: - **Correctness**: Answer accuracy - **Format Quality**: Proper structure and presentation - **Semantic Coherence**: Logical consistency in reasoning steps ### Training Process 1. **Problem Analysis**: Difficulty assessment and strategy selection 2. **ToT Exploration**: Multi-path reasoning with adaptive depth 3. **Path Evaluation**: Quality scoring of reasoning chains 4. **Supervised Fine-Tuning**: Training on high-quality reasoning examples 5. **Reward Optimization**: RLVR-style reinforcement of effective patterns ## 📊 Training Data Trained on diverse reasoning tasks from multiple domains: - **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 Data selection emphasizes: - High variance in difficulty - Multiple reasoning types (math, logic, pattern recognition) - Problems requiring multi-step solutions ## 🚀 Usage ### Basic Generation ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model = AutoModelForCausalLM.from_pretrained( "ziadrone/airesupdated-v3", torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained( "ziadrone/airesupdated-v3", trust_remote_code=True ) prompt = "Solve step by step: If 5 machines take 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=256, temperature=0.7, do_sample=True, top_p=0.9 ) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) ``` ### Recommended Settings For best reasoning performance: - **Temperature**: 0.6-0.8 (balance between creativity and consistency) - **Max tokens**: 256-512 (allow space for detailed reasoning) - **Top-p**: 0.9 (diverse but focused generation) - **Do sample**: True (enable exploration of reasoning paths) ## 🎓 Key Features ### Strengths - **Complex reasoning**: Excels at multi-step problems requiring decomposition - **Logical deduction**: Strong performance on syllogistic reasoning - **Pattern recognition**: Effective at identifying and extending sequences - **Self-correction**: Able to validate and revise reasoning chains ### Intended Use Cases - Mathematical problem solving - Logical reasoning and deduction - Pattern recognition and completion - Multi-step planning tasks - Educational applications requiring detailed explanations ### Limitations - Still under active evaluation - Performance varies with problem domain - May occasionally over-explain simple problems - Reasoning quality depends on prompt clarity ## ⚙️ Technical Details ### Model Specifications - **Base Model**: Qwen3-4B - **Parameters**: ~4 billion - **Precision**: BFloat16 - **Context Length**: Inherited from base (typically 32K tokens) - **Architecture**: Decoder-only transformer ### Training Infrastructure - **Hardware**: NVIDIA RTX A6000 (48GB VRAM) - **Framework**: PyTorch + Transformers - **Optimization**: AdamW with gradient accumulation - **Mixed Precision**: Enabled for memory efficiency ### Training Configuration - Adaptive temperature (0.6-1.0) during exploration - Multi-episode training per sample - Gradient checkpointing for memory optimization - Checkpoint saving at regular intervals ## 🔬 Research Background This model implements techniques inspired by: - **ARIES** (Autonomous Reasoning with Interactive Thought Graphs): Multi-path reasoning exploration - **RLVR** (Reinforcement Learning with Verifiable Rewards): Reward-guided training on reasoning tasks - **Tree-of-Thoughts**: Systematic exploration of reasoning chains The training methodology represents an experimental approach to improving reasoning capabilities in language models through structured exploration and reward-based learning. ## 📋 Evaluation Currently under active evaluation across multiple benchmarks including: - Mathematical reasoning (MATH, GSM8K) - Logical reasoning (ARC, HellaSwag) - Common sense reasoning - Multi-step problem solving *Results will be updated as evaluation completes.* ## 🔄 Version History - **v3**: Current version with improved ToT exploration and multi-agent policies - Active development - check back for updates ## ⚠️ Important Notes ### Development Status This model is part of ongoing research into reasoning enhancement techniques. Performance characteristics are still being evaluated and may be updated as testing continues. ### Reproducibility The training pipeline is designed to be reproducible. Similar improvements have been observed across multiple training runs, indicating stable methodology. ### Comparison with Base Fine-tuned specifically for reasoning tasks. For general-purpose use cases, the base Qwen3-4B may be more appropriate. ## 📄 License Apache 2.0 - Inherits license from base Qwen3-4B model. ## 🙏 Acknowledgments - **Qwen Team** at Alibaba Cloud for the excellent base model - **ARIES and RLVR research teams** for methodological inspiration - Open-source reasoning datasets used in training ## 📧 Contact & Feedback This is an experimental research model. Feedback on reasoning quality and performance is welcome and will help guide future improvements. --- **Note**: This model is optimized for reasoning tasks. For simple queries or general conversation, standard Qwen3-4B may be more efficient. Performance is best on problems that benefit from structured, multi-step reasoning. evals are in process with comparisons