Codes for StepTool

0. Environment Setup

1. Create a new Conda environment:

   conda create -n steptool python=3.10

2. Activate the environment:

   conda activate steptool

3. Install Pytorch and other required dependencies via pip:

    pip install torch torchvision torchaudio
    pip install -r requirements.txt

Note: Ensure that the version of GCC/G++ is >= 9.0.0.

1. Step-grained Data Construction

Step-grained rewards can be assigned using various methods, including automated rule-based systems, human annotations, or advanced models such as GPT-4.

Below is a reference prompt for GPT-4 to perform step-grained reward annotation:

Query:
{query}

Intermediate Steps:
{mid_steps}
Final Answer:
{final_answer}

Given the above query, all intermediate steps and the final answer, you need to evaluate the entire task-solving process by following rules:
(1) **Successful Tool Calling:** For each intermediate step, determine if a tool was called successfully and give a score of 0 (no) or 1 (yes).
(2) **Contribution to Final Answer:** For each intermediate step, rate its contribution to the final answer on a scale from 0 to 5.
(3) **Final Answer Status:** Determine if the final answer is 'Solved',  'Unsure', or 'Unsolved'.

Now provide your evaluation in JSON format with the parameters of 'succeed_tool_calling', 'contribution_to_final_answer' and 'final_answer_status'  to the function `evaluate_process_reward`.

We provide a sample training data file, data_train/${MODEL_TYPE}/step_grained_for_ppo_example.csv, for use in the subsequent training phase.

The complete training dataset can be downloaded from this Dropbox link.

2. Step-grained Training with PPO

The step-grained training is implemented in src/steptool/step_ppo.py and src/steptool/step_ppotrainer.py.

1. Configuration

Modify the configuration file config/${MODEL_TYPE}/StepTool_ppo.json as needed. The MODEL_TYPE can be one of toollama, qwen2, or llama3-1. Here’s an example configuration:

{
    "peft_kwargs": {
        "r": 8,
        "lora_alpha": 16,
        "bias": "none",
        "task_type": "CAUSAL_LM"
    },
    "ppo_kwargs": {
        "learning_rate": 1e-5,
        "log_with": "wandb",
        "remove_unused_columns": false,
        "batch_size": 8,
        "mini_batch_size": 2,
        "gradient_accumulation_steps": 4,
        "kl_penalty": "kl",
        "init_kl_coef": 0.3,
        "target_kl": 6,
        "target": 6,
        "horizon": 10000,
        "gamma": 0.99
    }
}

2. Run the scripts

bash scripts/steptool_train/train_toolllama.sh
bash scripts/steptool_train/train_qwen2.sh
bash scripts/steptool_train/train_llama3-1.sh

Example Command (from scripts/steptool_train/train_toolllama.sh):

export PYTHONPATH=./
export TRAIN_PATH="data_train"
export TRAIN_SET="step_grained_for_ppo_example"
export CUDA_VISIBLE_DEVICES="0,1,2,3"

export MODEL_TYPE="toolllama"
# load the base model after sft pretrain
export MODEL_PATH="ToolBench/ToolLLaMA-2-7b-v2"

python src/steptool/step_ppo.py \
    --model_path ${MODEL_PATH} \
    --model_type ${MODEL_TYPE} \
    --config_path config/${MODEL_TYPE}/StepTool_ppo.json \
    --data_file ${TRAIN_PATH}/${MODEL_TYPE}/${TRAIN_SET}.csv \
    --max_context_len 4096 \
    --max_response_len 1024 \
    --epochs 5

Note, for qwen2 and llama3.1, these models must undergo supervised fine-tuning (SFT) beforehand:

bash scripts/sft/train_qwen2.sh
bash scripts/sft/train_llama3-1.sh

A sample training dataset for SFT is available in data_train/${MODEL_TYPE}/gpt4_dfs_G123_for_sft_example.json

Train Baselines (RFT, PPO, ETO, ArCHer)

RFT

bash scripts/baseline-rft/train_rft.sh

PPO (Final Reward)

bash scripts/baseline-ppo/train_ppo.sh

ETO (DPO)

bash scripts/baseline-eto/train_dpo.sh

ArCHer

bash scripts/baseline-archer/build_data.sh
bash scripts/baseline-archer/train_archer.sh

Evaluation on StableToolBench

1. Build the api server

To set up the API server, follow the StableToolBench instructions.

First, download a cache from HuggingFace or Tsinghua Cloud.

After downloading, unzip the folder into the stabletoolbench/server folder and ensure the server folder contains tool_response_cache folder and tools folder. The resulting folder of server looks like:

├── /server/
│  ├── /tools/
│  │  └── ...
│  ├── /tool_response_cache/
│  │  └── ...
│  ├── config.yml
│  ├── main.py
│  ├── utils.py

Next, specify your configurations in server/config.yml

api_key: 
api_base: 
model: gpt-4-turbo-preview
temperature: 0
toolbench_url: http://8.130.32.149:8080/rapidapi
rapidapi_key: 
tools_folder: "./tools"
cache_folder: "./tool_response_cache"
is_save: true
port: 8081

To run the server:

cd server
python main.py

The server will be run at http://localhost:{port}/virtual. To use the server, you will further need a toolbench key. You can apply one from this form.

2. Run the model using vLLM

We recommend setting up a new Conda environment for vLLM by following the installation guide

To build a vLLM server for the ToolLLaMA-2-7b-v2 model, you can use the following command:

python -m vllm.entrypoints.openai.api_server --model ToolBench/ToolLLaMA-2-7b-v2 --served-model-name toolllama --max-model-len=8192 --dtype=bfloat16 --host 127.0.0.1 --port 8083 --rope-scaling '{"type": "linear", "factor": 2.0}'

Note: If you're using a LoRA version of the model, make sure to merge the LoRA weights with the base model before running it in vLLM.

3. Run the Evaluation Scripts

To evaluate the model on StableToolBench, first configure stabletoolbench/config.yml:

api_key:
api_base:
toolbench_key:
tool_root_dir: server/tools

Then, infer the model on the solvable_test_queries by running:

bash scripts_eval/toolllama/inference_toolllama_vllm.sh
bash scripts_eval/qwen2/inference_qwen2_vllm.sh
bash scripts_eval/llama3-1/inference_llama3-1_vllm.sh
bash scripts_eval/baseline-rft/inference_rft_vllm.sh
bash scripts_eval/baseline-ppo/inference_ppo_vllm.sh
bash scripts_eval/baseline-eto/inference_eto_vllm.sh
bash scripts_eval/baseline-archer/inference_archer_vllm.sh

Finally, evaluate the pass_rate and win_rate metrics:

bash scripts_eval/toolllama/run_convert_answer.sh
bash scripts_eval/toolllama/run_pass_rate.sh
bash scripts_eval/toolllama/run_preference.sh

bash scripts_eval/qwen2/run_convert_answer.sh
bash scripts_eval/qwen2/run_pass_rate.sh
bash scripts_eval/qwen2/run_preference.sh

bash scripts_eval/llama3-1/run_convert_answer.sh
bash scripts_eval/llama3-1/run_pass_rate.sh
bash scripts_eval/llama3-1/run_preference.sh

bash scripts_eval/baseline-rft/run_convert_answer.sh
bash scripts_eval/baseline-rft/run_pass_rate.sh

bash scripts_eval/baseline-ppo/run_convert_answer.sh
bash scripts_eval/baseline-ppo/run_pass_rate.sh

bash scripts_eval/baseline-eto/run_convert_answer.sh
bash scripts_eval/baseline-eto/run_pass_rate.sh

bash scripts_eval/baseline-archer/run_convert_answer.sh
bash scripts_eval/baseline-archer/run_pass_rate.sh

Main Experimental Results in the Paper (Updated in May. 2025)

All results were re-evaluated in May. 2025 to ensure the stability of the \texttt{gpt-4-turbo-2024-04-09} evaluator.

Backbone	Strategy	Method	I1. Pass	I1. Recall	I2. Pass	I2. Recall	I3. Pass	I3. Recall	ToolLens Test. Pass	ToolLens Test. Recall
ToolLLaMA-2-7b-v2	CoT	SFT	50.6±1.6	0.7952	47.1±0.8	0.8081	40.4±0.8	0.6833	40.2±0.9	0.6769
ToolLLaMA-2-7b-v2	CoT	RFT	50.2±1.2	0.8061	45.9±1.8	0.8197	38.5±1.2	0.7536	39.5±0.7	0.7323
ToolLLaMA-2-7b-v2	CoT	PPO (Final Reward)	50.9±1.0	0.8030	46.6±2.0	0.8185	40.2±0.0	0.6869	39.2±0.1	0.6817
ToolLLaMA-2-7b-v2	CoT	ETO (DPO)	50.3±0.9	0.7874	45.9±0.3	0.7859	38.8±1.0	0.7150	40.2±0.5	0.7176
ToolLLaMA-2-7b-v2	CoT	ArCHer	51.8±0.8	0.8005	47.5±0.6	0.8039	35.5±2.8	0.6907	42.8±0.6	0.6693
ToolLLaMA-2-7b-v2	CoT	StepTool	61.1±0.7	0.8743	56.6±2.2	0.8992	45.9±1.8	0.7724	47.3±0.4	0.7500
-----	-----	-----	---------	---------	---------	---------	---------	---------	---------	---------
ToolLLaMA-2-7b-v2	DFSDT	SFT	58.7±1.0	0.8419	54.3±1.1	0.8665	54.1±1.3	0.7331	46.6±1.0	0.7092
ToolLLaMA-2-7b-v2	DFSDT	RFT	55.0±1.6	0.8490	49.5±0.8	0.8531	58.5±2.4	0.7465	41.1±0.5	0.7598
ToolLLaMA-2-7b-v2	DFSDT	PPO (Final Reward)	59.6±1.1	0.8360	54.0±1.6	0.8709	39.9±0.8	0.7344	42.4±0.5	0.7004
ToolLLaMA-2-7b-v2	DFSDT	ETO (DPO)	57.1±1.2	0.8412	54.5±2.1	0.8747	44.0±2.8	0.7298	47.9±0.8	0.7487
ToolLLaMA-2-7b-v2	DFSDT	ArCHer	60.0±1.5	0.8491	54.5±0.8	0.8724	53.3±2.0	0.7284	45.6±0.3	0.7207
ToolLLaMA-2-7b-v2	DFSDT	StepTool	64.1±1.4	0.8797	60.3±0.9	0.9004	64.8±2.3	0.7831	53.2±1.2	0.7819

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