OLMo-3-7B-RLZero-Math

A 7B math reasoning model from Allen AI, trained with RL-Zero (RLVR) to solve problems step-by-step like a skilled tutor. Runs on any laptop.

🚀 Overview

OLMo-3-7B-RLZero-Math is a 7-billion-parameter mathematical reasoning model built on Allen AI’s fully open OLMo 3 family. It is trained with RL-Zero, reinforcement learning from verifiable rewards (RLVR), without human demonstrations, on the Dolci-RL-Zero-Math dataset of math queries. The result is a model that works through algebra, calculus, word problems, and proofs by showing its reasoning clearly, like a patient tutor.

These RL-Zero checkpoints are part of Allen AI’s open scientific exploration of RLVR training. Everything in the OLMo 3 flow is open: data, code, weights, and training recipes.

🎯 Key Features

• Step-by-Step Reasoning: Works problems out explicitly, showing its work like a skilled tutor
• Math-Specialized: Tuned for algebra, calculus, word problems, and mathematical proofs
• RL-Zero Training: Reinforcement learning from verifiable rewards (RLVR), no human demonstrations
• Long Context: 65,536-token window for complex, multi-step problems
• Fully Open: Built on Allen AI’s open OLMo 3 family, open data, weights, and recipes
• Laptop-Friendly: Quantized builds run comfortably on standard hardware

🏷️ Available Versions

💻 Quick Start

# Pull and run (recommended Q4_K_M)
ollama run richardyoung/olmo-3-7b-rlzero-math

# Or pick a specific version
ollama run richardyoung/olmo-3-7b-rlzero-math:iq3_m    # Smallest
ollama run richardyoung/olmo-3-7b-rlzero-math:Q5_K_M   # Higher quality
ollama run richardyoung/olmo-3-7b-rlzero-math:q8_0     # Near-lossless

🛠️ Use Cases

• Math Tutoring: Step-by-step walkthroughs of algebra, calculus, and word problems
• Reasoning Research: A clean, open baseline for studying RLVR / RL-Zero training
• Education: Explain mathematical concepts and proofs with visible working
• STEM Assistance: Support quantitative reasoning in study and analysis workflows

📋 System Requirements

CPU-only inference is also supported and runs well on modern laptops with the smaller quantizations.

🔧 Technical Details

Base Model: allenai/Olmo-3-1025-7B Parameters: 7B Architecture: Decoder-only transformer (32 layers, 4096 hidden size) Context Length: 65,536 tokens Training Method: RL-Zero, reinforcement learning from verifiable rewards (RLVR) on Dolci-RL-Zero-Math License: Apache 2.0

⚠️ Disclaimer

The OLMo 3 RL-Zero models are experimental research checkpoints intended for the scientific exploration of RLVR training. Verify mathematical results independently before relying on them, and use the model in accordance with applicable laws and the Apache 2.0 license.

🙏 Acknowledgments

• Base Model & Training: allenai/Olmo-3-7B-RL-Zero-Math by Allen Institute for AI (Ai2)
• OLMo 3 Project: Ai2 OLMo 3
• Quantization: llama.cpp

Built & maintained by Richard Young · DeepNeuro