qwen3-0.6b-rlhf-cot

Qwen3-0.6B-RLHF-CoT: Chain-of-Thought via Pure RL

Fine-tuned Qwen/Qwen3-0.6B-Base using pure reward-based reinforcement learning without SFT.

🎯 Methodology: Inspired by DeepSeek-R1-Zero

This model follows the approach from DeepSeek-R1-Zero:

• ❌ No SFT - Starts from base model
• ✅ Pure RL - Only reward signals guide learning
• ✅ CoT emerges naturally through trial and error
• ✅ Reward-guided training - Model learns from reward feedback

📊 Training Details

Model Architecture

• Base Model: Qwen/Qwen3-0.6B-Base (606M parameters)
• Value Head: Added for reward-based learning
• LoRA: r=16, alpha=32 (1.67% trainable parameters - 10M params)
• Quantization: 4-bit (NF4) for efficient training on consumer GPUs

Training Configuration

• Dataset: 1000 samples from nvidia/Aegis-AI-Content-Safety-Dataset-2.0
• Method: Reward-weighted gradient descent (simplified RLHF)
• Epochs: 1
• Batch Size: 64
• Learning Rate: 5e-05
• Optimizer: Adam with gradient accumulation (steps: 4)
• Generation Length: 64 tokens
• Training Mode: Quick Test - trains every 10 batches (~10% of data)
• Speed Optimizations: Ultra-fast mode with large batches, sparse training, and short generation

Reward Function

The model was trained with a reward function that encourages:

• Reasoning markers: "First," "Therefore," "Let's analyze," etc. (+0.5 per marker, max +3.0)
• Structured thinking: Multi-step, multi-sentence responses (+1.5 for 3+ sentences)
• Appropriate depth: 30-300 tokens sweet spot (+2.0)
• Domain awareness: Content safety concepts and terminology (+0.3 per keyword, max +2.0)
• Coherence: Penalties for repetitive outputs (-2.0 for <50% unique words)
• Explicit reasoning: Bonus for "reasoning:" or "analysis:" sections (+1.0)

🚀 Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ahczhg/qwen3-0.6b-rlhf-cot")
model = AutoModelForCausalLM.from_pretrained("ahczhg/qwen3-0.6b-rlhf-cot")

prompt = '''Think step-by-step and reason carefully about this content safety query.

Query: Is it safe to share personal banking information in a public forum?

Reasoning:'''

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.9)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

🧠 How CoT Emerges

Unlike traditional approaches that explicitly train models on reasoning examples:

1. Base model starts with no reasoning capability
2. Generates responses to content safety queries
3. Receives rewards based on reasoning quality (scored by reward function)
4. Learns to maximize rewards by developing CoT patterns
5. Emerges natural reasoning without being explicitly taught

This demonstrates that chain-of-thought reasoning can be learned purely through reward optimization!

📈 Training Process

The training uses a simplified RLHF approach with speed optimizations:

1. Generate: Model produces responses for each query (64 tokens)
2. Score: Reward function evaluates CoT quality
3. Train: Gradient updates on positively-rewarded examples (every 10 batches)
4. Iterate: Gradually improve reasoning over epochs

Speed Optimizations (Quick Test Mode)

To enable fast pipeline testing, this model used:

• Large batches: 64 samples per batch (2-8x larger than typical)
• Sparse training: Updates only every 10 batches (10% of data)
• Short generation: 64 tokens (vs 256+ for quality)
• Higher learning rate: 5e-05 for faster convergence
• Gradient accumulation: 4 steps for efficiency
• Progress tracking: tqdm bars for monitoring

Expected training time: ~5-10 minutes on A100 GPU

📚 References

• Inspiration: DeepSeek-R1-Zero - Pioneered no-SFT RL for CoT
• Base Model: Qwen3-0.6B-Base - Alibaba's efficient base model
• Dataset: Aegis AI Content Safety - NVIDIA's safety dataset

⚠️ Limitations

• Small scale: 1000 samples (vs. millions for DeepSeek-R1-Zero)
• Small model: 0.6B parameters may limit reasoning depth
• Experimental: Proof-of-concept demonstrating the methodology
• Simplified RL: Uses reward-weighted training instead of full PPO
• Domain-specific: Focused on content safety tasks
• Training mode: Trained in ultra-fast test mode for rapid iteration
• Sparse updates: Only ~10% of data used for weight updates
• Short generation: 64 tokens may limit reasoning depth during training

💡 Key Insights

This model demonstrates:

• ✅ Chain-of-thought can emerge without explicit reasoning examples
• ✅ Reward design is critical for guiding reasoning development
• ✅ Even simple reward-based RL can encourage structured thinking
• ✅ Alternative to expensive human-annotated CoT datasets
• ✅ Efficient training via LoRA + 4-bit quantization on consumer GPUs
• ✅ Speed vs quality tradeoffs in RLHF training

🔬 For Production Use

To improve this approach:

1. Scale up: Train on 100K+ samples
2. Larger model: Use 7B+ parameter models
3. More epochs: Train longer (3-5 epochs minimum)
4. Full training: Train every batch, not every 10th
5. Longer generation: Use 256+ tokens during training
6. Refined rewards: Iterate on reward function based on evaluation
7. Full PPO: Implement complete PPO algorithm for better RL
8. Human evaluation: Assess reasoning quality with human raters

🛠️ Training Speed vs Quality

This model prioritized speed for testing:

• ⚡ Quick Test Mode: 64 batch size, train every 10 batches, 64 tokens
• 🎯 For production: Set QUICK_TEST=False for full quality training

The ultra-fast mode processes 640 samples between weight updates, enabling rapid iteration on the training pipeline while still demonstrating the core methodology.

📄 License

Apache 2.0 (inherited from Qwen3-0.6B-Base)

🙏 Acknowledgments

• DeepSeek-AI for pioneering the no-SFT RL approach
• Alibaba Qwen Team for the excellent base model
• NVIDIA for the Aegis content safety dataset
• HuggingFace TRL team for RL training tools

📌 Citation

If you use this model or methodology, please cite:

@misc{qwen3-rlhf-cot-2025,
  title={Qwen3-0.6B-RLHF-CoT: Chain-of-Thought via Pure Reinforcement Learning},
  author={ahczhg},
  year={2025},
  publisher={HuggingFace},
  url={https://huggingface.co/ahczhg/qwen3-0.6b-rlhf-cot}
}

And the original DeepSeek-R1-Zero work:

@misc{deepseek-r1-zero,
  title={DeepSeek-R1-Zero},
  author={DeepSeek-AI},
  year={2024},
  publisher={HuggingFace},
  url={https://huggingface.co/deepseek-ai/DeepSeek-R1-Zero}
}

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🤖 Training Method: Reward-based RL without SFT, inspired by DeepSeek-R1-Zero
⚡ Training Speed: Ultra-fast test mode - optimized for rapid pipeline testing (~5-10 min on A100)
📊 Training Coverage: ~10% of data (sparse updates for speed)