DeepRE

Created by Vivek, inspired by the groundbreaking work of Jiayi Pan, an AI researcher at Berkeley University.

DeepRE is a reproduction of DeepSeek R1 Zero in countdown and multiplication tasks. We built upon veRL.

Through RL, the 3B base LM develops self-verification and search abilities all on its own

You can experience the Ahah moment yourself for < $30

Full experiment log: on it's way!

Paper's on it's way!

Installation

conda create -n zero python=3.9
# install torch [or you can skip this step and let vllm to install the correct version for you]
pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu121
# install vllm
pip3 install vllm==0.6.3 # or you can install 0.5.4, 0.4.2 and 0.3.1
pip3 install ray

# verl
pip install -e .

# flash attention 2
pip3 install flash-attn --no-build-isolation
# quality of life
pip install wandb IPython matplotlib

Countdown task

Data Preparation

conda activate zero
python ./examples/data_preprocess/countdown.py --local_dir {path_to_your_dataset}

Run Training

conda activate zero

For the following code, if you see Out-of-vram, try add critic.model.enable_gradient_checkpointing=True to the script, Single GPU

Works for model <= 1.5B. For Qwen2.5-0.5B base, we know it fails to learn reasoning.

export N_GPUS=1
export BASE_MODEL={path_to_your_model}
export DATA_DIR={path_to_your_dataset}
export ROLLOUT_TP_SIZE=1
export EXPERIMENT_NAME=countdown-qwen2.5-0.5b
export VLLM_ATTENTION_BACKEND=XFORMERS

bash ./scripts/train_tiny_zero.sh

3B+ model In this case, the base model is able to develop sophisticated reasoning skills.

export N_GPUS=2
export BASE_MODEL={path_to_your_model}
export DATA_DIR={path_to_your_dataset}
export ROLLOUT_TP_SIZE=2
export EXPERIMENT_NAME=countdown-qwen2.5-3b
export VLLM_ATTENTION_BACKEND=XFORMERS

bash ./scripts/train_tiny_zero.sh

Instruct Ablation

We experiment with QWen-2.5-3B Instruct too. Data Preparation To follow chat template, we need to reprocess the data:

conda activate zero
python examples/data_preprocess/countdown.py --template_type=qwen-instruct --local_dir={path_to_your_dataset}

Training

export N_GPUS=2
export BASE_MODEL={path_to_your_model}
export DATA_DIR={path_to_your_dataset}
export ROLLOUT_TP_SIZE=2
export EXPERIMENT_NAME=countdown-qwen2.5-3b-instruct
export VLLM_ATTENTION_BACKEND=XFORMERS

bash ./scripts/train_tiny_zero.sh

Acknowledge

• We run our experiments based on veRL.
• We use Qwen2.5 series base model Qwen2.5.