ASCENT

long CoT training data: huggingface dataset

Set up enviroment

pip install vllm>=0.6.6.post1

Main steps

Don't forget to set your own file and model paths in the scripts.

Stage 1

cd ASCENT

# use VLLM to deploy the model used to generate sft data.
bash scripts/run_generative_model_server.sh

# generate sft data for stage 1
bash scripts/stage1_generation.sh

# use VLLM to deploy Bradley-Terry reward model.
bash scripts/run_reward_model_server.sh

# # scoring (for reject sampling) (Set "output-structure" in the code to "sft")
bash scripts/stage1_scoring.sh

# prepare training data
python src/data/prepare_sft_data.py

# train your RSFT model with the frameworks like LLaMA-Factory (https://github.com/hiyouga/LLaMA-Factory.git)

# use VLLM to deploy the RSFT model.
bash scripts/run_generative_model_server.sh

# generate dpo data for stage 1 (Set "model-name" in the code to your RSFT model)
bash scripts/stage1_generation.sh

# scoring (for reject sampling) (Set "output-structure" in the code to "dpo")
bash scripts/stage1_scoring.sh

# prepare dpo training data
python src/data/prepare_dpo_data.py

# train your DPO model with the frameworks like LLaMA-Factory (https://github.com/hiyouga/LLaMA-Factory.git)

Stage 2

# use VLLM to deploy stage 1 model.
bash scripts/run_generative_model_server.sh

# use VLLM to deploy Bradley-Terry reward model.
bash scripts/run_reward_model_server.sh

# generate sft data for stage 2 in a greedy search way.
bash scripts/cot_generation.sh

# prepare training data
python src/data/prepare_cot_sft_data_greedy.py

# train your RSFT model with the frameworks like LLaMA-Factory (https://github.com/hiyouga/LLaMA-Factory.git)

# use VLLM to deploy the RSFT model.
bash scripts/run_generative_model_server.sh

# generate dpo data for stage 2
bash scripts/cot_dpo_generation.sh

# scoring (for reject sampling)
bash scripts/cot_dpo_scoring.sh

# prepare dpo training data (for length control)
python src/data/prepare_cot_dpo_data.py

# train your DPO model with the frameworks like LLaMA-Factory (https://github.com/hiyouga/LLaMA-Factory.git)

Evaluation

Refer to README

Citation