Revolutionary One-Shot RLVR: Train LLMs to Reason with a Single Example

Imagine training a large language model (LLM) to perform complex reasoning tasks using just one training example. That's the promise of One-Shot RLVR (Reinforcement Learning for Reasoning), a groundbreaking approach detailed in a recent paper. This innovative technique dramatically reduces the data and computational resources required for training, opening up new possibilities for efficient LLM development. This method effectively lowers the barrier to entry for those looking to train high-performance models.

What is One-Shot RLVR and Why Should You Care?

One-Shot RLVR leverages reinforcement learning to fine-tune LLMs, enabling them to excel at tasks like mathematical reasoning even with limited data. Here's why it's a game-changer:

• Data Efficiency: Train your LLM with a single example, drastically reducing data collection and annotation efforts.
• Resource Savings: Lower computational costs due to the minimal training data required.
• Faster Development: Accelerate the development cycle of reasoning-capable LLMs.

Setting Up Your Environment for One-Shot RLVR Training

Ready to dive in? Here's how to set up your training and evaluation environments:

Training Environment:

1. Create a new conda environment:

   conda create -y -n rlvr_train python=3.10
   

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2. Activate the environment:

   conda activate rlvr_train
   

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3. Install the necessary packages:

   pip install -e.
   pip3 install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu121
   pip3 install ray vllm==0.6.3
   pip3 install flash-attn --no-build-isolation
   pip install wandb matplotlib
   pip install huggingface_hub
   

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Evaluation Environment:

1. Create a new conda environment:

   conda create -y -n rlvr_eval python=3.10
   

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2. Activate the environment:

   conda activate rlvr_eval
   

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3. Install the required packages using the instructions from Qwen2.5-Math, ensuring compatibility.

Data Preparation for One-Shot RLVR

The core of One-Shot RLVR lies in its efficient data utilization. The researchers used a subset of the DeepScaleR-Preview-Dataset (DSR-sub). To get started, you'll need:

• DSR-sub: A subset of 1209 examples from DeepScaleR, used as the instance pool for data selection.
• One-Shot Example: The training example used in the paper is included in data/train/one_shot_rlvr.
• (Optional) Variance Ranking: You can rank the DSR-sub dataset by historical variance score to select potentially better training examples.

Training Your LLM with One-Shot RLVR

With your environment and data ready, you can begin training. Follow these steps:

1. Set the checkpoint path:

   export CHECKPOINTS_DIR=./checkpoints/ # your checkpoint path
   

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2. Run the training script:

   conda activate rlvr_train
   bash scripts/train/training_1.5b_pi1_r128.sh
   

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This script trains your model using the one-shot approach. Fine-tuning Large Language Models has never been easier!

Evaluating Your One-Shot RLVR Model

After training, it's crucial to evaluate your model's performance. Here's how:

1. Activate the evaluation environment:

   conda activate rlvr_eval
   

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2. Navigate to the evaluation directory:

   cd Qwen2.5-Eval/evaluation
   

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3. Run the evaluation script:

   bash sh/eval_one_experiment_all_ckpts.sh
   

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This script evaluates your model on math reasoning benchmarks like MATH500, AIME24, and more. You can now effectively evaluate your model's ability in One-Shot Reinforcement Learning for Reasoning.

Diving Deeper: W&B Experiment Tracking

The researchers logged their experiments on Weights & Biases (W&B). This includes results for:

• One-Shot RLVR on Qwen2.5-Math-1.5B and Qwen2.5-Math-7B.
• Full-set RLVR with DSR-sub as a baseline.
• DeepSeek-R1-Distill-Qwen-1.5B. wandb project Example

Note: Validation results displayed in W&B may differ slightly from qwen-eval results due to the framework used.

Conclusion: The Future of LLM Training is Here with One-Shot RLVR

One-Shot RLVR represents a significant step forward in LLM training. By enabling effective reasoning with minimal data, it democratizes access to advanced AI capabilities. Experiment with One-Shot RLVR and contribute to this exciting field! This method is specifically impactful to Reinforcement Learning for Reasoning in Large Language Models.