This repository implementats our experiments with synthetic preference data in the paper Group Robust Preference Optimization in Reward-free RLHF.

We build upon the following codebase https://github.com/liziniu/policy_optimization that implements DPO training for bandit environments.

The main scripts relevant for group robust DPO and IPO are scripts/run_glp_mult.sh which calls upon experiments/run_group_linear_bandit_sep_theta_combined_det_ratio.

They use the Group Linear Bandit environment in envs/group_linear_bandit.py. The algorithms used are group DPO/IPO written together in algos/linear_bandit/group_dpo.py and group robust DPO/IPO in algos/linear_bandit/group_robust_dpo.py.

The algos/vectorized_dpo.py improves the DPO implementation in terms of vectorization and linear gradients avoiding numerical instability.

The Python environment can be set up using Anaconda with the provided environment.yml file.

conda env create -f environment.yml
conda activate bandit

The main experiment file is experiments/run_group_linear_bandit_sep_theta_combined_uneven_grp_vectorised.py. This file is called from scripts/run_glp_mult_uneven.sh. Due to the large number of command-line arguments, we also provide scripts in scripts/scripts\ to\ reproduce, which only alter significant arguments and call scripts/run_glp_mult_uneven.sh.

The important arguments are:

• dpo_type : 'dpo' for non-robust baseline, and 'rdpo' for robust GRPO.
• ipo_grad_type : 'justdpo' for DPO loss (this is GR-DPO if dpo_type = rdpo or DPO/IS-DPO if dpo_type = dpo); 'linear' for IPO loss (GR-IPO if dpo_type = rdpo or IPO/IS-IPO if dpo_type = dpo)
• importance_sampling & importance_sampling_weights : If importance_sampling = true and importance_sampling_weights are given as a string-array, IS-DPO/IS-IPO. Elif importance_sampling = false, DPO/IPO. Here, one must concurrently set dpo_type = dpo.
• feature_type : 'swapped', 'flipped' or 'same' feature vector φ(x,y,g).
• weight : Percentage of training samples given to the first group (2 groups case); weight=0.2 is imbalanced-data (20-80 imbalance), and weight=0.5 is balanced-data (50-50).

Call scripts/scripts\ to\ reproduce scripts to run (1) Even-Imbalanced, (2) Uneven-Balanced, or (3) Uneven-Imbalanced configurations. The call is as follows

bash scripts/scripts\ to\ reproduce/even_group_imbalanced_data.sh
bash scripts/scripts\ to\ reproduce/uneven_group_balanced_data.sh
bash scripts/scripts\ to\ reproduce/uneven_group_imbalanced_data.sh

All synthetic experiments run for 20 seeds (2021-2040), in CPU compute (Intel Xeon E5-4620 v4 @ 2.10GHz & Xeon E5-2660 v3 @ 2.60GHz).

The main plotting scripts are (1) plotting/wandb_data_collect_script_final_simplified.py and (2) plotting/wandb_data_collect_script_final_simplified_paperplots.py.

Script (1) generates plots as individual plt.figure objects. Script (2) generates a plt.subplots object with multiple aligned subplots. This is how we present in the paper the synthetic-experiment plots of worst-case GR-DPO validation loss, worst-case GR-IPO validation loss, and GR-DPO/GR-IPO converged reward error. In script (2), the metrics being plotted together in the subplot must be manually-specified.

When running experiments (Training and Evaluation), the WandB Group name should match the group names of the runs that are downloaded by the plot scripts (i.e. the WandB groups defined in the SETTINGS var -- Line 20, scripts (1) and (2).).

To run Script (2) for all configurations (even/uneven groups, balanced/imbalanced data), plotting/plotting.sh may be used. For each configuration, append _dpo in --setting for GR-DPO plots, _ipo for GR-IPO plots, and _all for GR-DPO & GR-IPO together.

Please cite our paper if you find the repo helpful in your work:

@article{ramesh2024grpo,
    title={Group Robust Preference Optimization in Reward-free RLHF},
    author={Shyam Sundhar Ramesh, Iason Chaimalas, Viraj Mehta, Haitham Bou Ammar, 
            Pier Giuseppe Sessa, Yifan Hu, Ilija Bogunovic},
    year={2024}
}