Update README.md

README.md

@@ -1,199 +1,123 @@
----
-library_name: transformers
-tags: []
----
+# OREO: Offline REasoning Optimization
 
-# Model Card for Model ID
+Source code for [Offline Reinforcement Learning for LLM Multi-Step Reasoning](https://arxiv.org/abs/2412.16145)
 
-<!-- Provide a quick summary of what the model is/does. -->
+Model: [Policy](https://huggingface.co/jwhj/Qwen2.5-Math-1.5B-OREO) | [Value](https://huggingface.co/jwhj/Qwen2.5-Math-1.5B-OREO-Value)
 
+<img src="https://raw.githubusercontent.com/jwhj/OREO/refs/heads/main/OREO.png" alt="Image description" width="50%" />
 
 
-## Model Details
+# Installation
 
-### Model Description
+This repo is based on [OpenRLHF](https://github.com/OpenRLHF/OpenRLHF) and the installation follows a similar process. We recommend using Docker to setup the environment.
 
-<!-- Provide a longer summary of what this model is. -->
+First build Docker image
+```bash
+cd dockerfile
+docker build -t [IMAGE_NAME] .
+```
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+Start a docker container
+```bash
+docker run -itd --ipc host --gpus all [IMAGE_NAME] bash
+```
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+Attach to the container
+```bash
+docker exec -it [CONTAINER_ID] /bin/bash
+```
 
-### Model Sources [optional]
+Install the current repo
+```bash
+cd [PATH_TO_THIS_REPO]
+pip install -e .
+```
 
-<!-- Provide the basic links for the model. -->
+As the data collection process involves randomness, we will publish the training data used in our experiments in the near future.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+# Reproduction
+## Training
+You may need to change the following command line options in the following scripts:
+- `--train_file` specifies the path of training data in OREO experiments.
+- `--dataset` specifies the path of training data in SFT experiments.
+- `--save_path` specifies the path to save the model.
+- `--pretrain` specifies the path to load the pretrained model. In OREO experiments, this should be the path to the SFT model.
 
-## Uses
+### Math Reasoning
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Supervised fine-tuning
+```bash
+cd example/scripts
+bash train_oreo_sft.sh
+```
 
-### Direct Use
+OREO training
+```bash
+cd example/scripts
+bash train_oreo.sh
+```
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+To train the `DeepSeekMath-7B-Instruct` model,
+```bash
+cd example/scripts
+bash train_oreo_deepseek-math.sh
+```
+Note that `DeepSeekMath-7B-Instruct` is already supervise fine-tuned, so we don't have an SFT phase here.
 
-[More Information Needed]
+### ALFWorld
 
-### Downstream Use [optional]
+Supervised fine-tuning
+```bash
+cd example/scripts
+bash train_oreo_alfworld_sft.sh
+```
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+OREO training
+```bash
+cd example/scripts
+bash train_oreo_alfworld.sh
+```
 
 ## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+### Math Reasoning
+
+Make sure you have `antlr4-python3-runtime==4.11.0` installed.
+
+For Qwen-based models
+```bash
+cd example/scripts
+python ../scratch/run_qwen.py --model [PATH_TO_YOUR_MODEL] --save [SAVE_GENERATED_RESULTS_JSONL]
+```
+
+For DeepSeekMath-based models
+```bash
+cd example/scripts
+python ../scratch/run_qwen.py --model [PATH_TO_YOUR_MODEL] --no_bos --save [SAVE_GENERATED_RESULTS_JSONL]
+```
+Note the `--no_bos` option here.
+
+### ALFWorld
+
+This part requires [ALFWorld](https://github.com/alfworld/alfworld) to be installed.
+
+First start an vllm server
+```bash
+python -m vllm.entrypoints.openai.api_server --model [PATH_TO_YOUR_MODEL]
+```
+
+Then run evaluation with
+```bash
+cd example/scripts
+python ../scratch/run_alfworld_async.py --model [PATH_TO_YOUR_MODEL] --save_dir [SAVE_GENERATED_TRAJS]
+```
+You can use `--split eval_in_distribution` for seen environments.
+
+## Reference
+```bibtex
+@inproceedings{Wang2024OfflineRL,
+  title={Offline Reinforcement Learning for LLM Multi-Step Reasoning},
+  author={Huaijie Wang and Shibo Hao and Hanze Dong and Shenao Zhang and Yilin Bao and Ziran Yang and Yi Wu},
+  year={2024},
+  url={https://api.semanticscholar.org/CorpusID:274965107}
+}
+```