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--- |
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license: apache-2.0 |
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base_model: thomasjhuang/qwen2-sft-warmup |
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tags: |
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- reinforcement-learning |
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- rloo |
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- countdown-math |
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- qwen2 |
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language: |
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- en |
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pipeline_tag: text-generation |
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--- |
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# Qwen2 RLOO Countdown (Step 150) |
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This model is a Qwen2-based language model fine-tuned using RLOO (Rank-order Learning with Localized Objectives) on countdown math problems. |
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## Training Details |
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- **Base Model**: thomasjhuang/qwen2-sft-warmup |
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- **Method**: RLOO (Reinforcement Learning from Human Feedback) |
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- **Dataset**: Jiayi-Pan/Countdown-Tasks-3to4 |
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- **Training Steps**: 150 optimizer steps |
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- **Learning Rate**: 3e-6 |
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- **Temperature**: 0.1 |
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- **Batch Size**: 2 |
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- **K Samples**: 8 |
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## Key Fixes Applied |
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1. **Prompt Format**: Updated to match SFT evaluation format with detailed instructions |
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2. **Token Length**: Increased to 250 tokens for complete reasoning |
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3. **Temperature**: Reduced to 0.1 for more deterministic generation |
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4. **Extraction**: Fixed to work with vLLM outputs |
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## Performance |
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During training at step 150, the model achieved: |
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- Average rewards ranging from 0.05 to 0.50 across batches |
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- Successful generation of proper `<think>` and `<answer>` tags |
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- Correct solutions to various countdown math problems |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("thomasjhuang/qwen2-rloo-countdown-step150") |
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model = AutoModelForCausalLM.from_pretrained("thomasjhuang/qwen2-rloo-countdown-step150") |
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prompt = '''Using the numbers [8, 16, 80], create an equation that equals 72. You can use basic arithmetic operations (+, -, *, /) and each number can only be used once. Show your work in <think> </think> tags. And return the final answer in <answer> </answer> tags, for example <answer> (1 + 2) / 3 </answer>.''' |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=300, temperature=0.1) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
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``` |
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## Training Progress |
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This checkpoint represents an intermediate state in RLOO training where: |
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- The model learned to follow the correct prompt format |
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- Success rates improved from 0% to 10-50% on various problems |
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- The model generates structured reasoning in `<think>` tags |
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- Solutions are properly formatted in `<answer>` tags |
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For the latest checkpoint, see: thomasjhuang/qwen2-rloo-countdown-final |
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