LoRe is a lightweight, modular codebase for learning personalized reward models from preference data in multi-user environments. It supports both joint reward learning and few-shot personalization, and is built with extensibility in mind.

LoRe currently supports experiments on three benchmark datasets:

• Reddit TLDR: headline preference modeling
• PRISM: multi-turn dialogue response preferences
• PersonalLLM: user-personalized reward modeling for open-ended language model responses

• Low-rank reward model learning across users
• Few-shot personalization with new users
• Evaluation on seen/unseen users and prompts
• Modular dataset and optimizer configuration

LoRe requires Python 3.8+ and PyTorch. To install dependencies:

pip install -r requirements.txt

LoRe/
├── utils.py                    # Core training, optimization, and evaluation helpers
├── RedditTLDR/                 # TLDR dataset scripts
│   ├── prepare.py              # Preprocess the dataset
│   ├── train_basis.py          # Train shared reward model and user weights
│   └── vary_fewshot.py         # Evaluate few-shot personalization
├── PRISM/                      # PRISM dataset scripts
│   ├── prepare.py
│   ├── train_basis.py
│   └── vary_fewshot.py
├── PersonalLLM/                # PersonalLLM dataset scripts
│   ├── prepare.py
│   ├── train_basis.py

The following functions are central to training and evaluating personalized reward models. You may want to modify these if you’re extending LoRe:

• LoRe(...): Class modeling shared reward model V (linear transformation on fixed embeddings) and user-specific weights W
• LoRe_regularized(...): Class modeling shared reward model V (linear transformation on fixed embeddings), cosine similarity regularization to base model, and user-specific weights W
• PersonalizeBatch(...): Class to model weights for new users

• run(...): Runs the entire pipeline with 1. Learning the basis rewards, 2. Evaluation on seen users, 3. Fewshot learning on new users, 4. Evaluation on new users. The input K_list can be modified to specify the number of basis. 0 is the reference model, and 1 is the BT model.
• run_regularized(...): Runs the entire pipeline as run(...) but with regularization on the final layer

Below are instructions for each dataset folder, following a consistent workflow:

1. Prepare the dataset if required.
2. Train the reward model basis using joint learning.
3. Evaluate few-shot personalization with unseen users.

Inside the RedditTLDR/ directory:

• prepare.py: preprocesses the TLDR dataset
• train_basis.py: trains the shared reward model and user weights
• vary_fewshot.py: evaluates few-shot personalization performance

Example usage:

cd LoRe/RedditTLDR
python prepare.py          # only needed once
python train_basis.py
python vary_fewshot.py

Inside the PersonalLLM/ directory:

• prepare.py: prepares model response data and user splits
• train_basis.py: learns reward basis across users
• vary_fewshot.py: evaluates few-shot generalization

Example usage:

cd LoRe/PersonalLLM
python prepare.py          # only needed once
python train_basis.py
python vary_fewshot.py

Inside the PRISM/ directory:

• prepare.py: prepares PRISM dialogue in chat format
• generate-prepare-embeddings.py: prepares PRISM embeddings
• train_basis.py: runs reward model training and evaluation
• eval_rm2.py: runs learn reward basis models on the reward bench 2 dataset

Example usage:

cd LoRe/PRISM
python prepare.py          # only needed once
python generate-prism-embeddings.py # only needed once
python train_basis.py # train the model for a list of ranks, default regularization is specified
python eval_rb2.py --rm_head "path to saved final layer (V) weights" # evaluate learnt reward basis on RewardBench2 to avoid overfitting to PRISM

Experiments on a much larger community alignment dataset for scalable, multi-user preference learning.

See the CONTRIBUTING file for how to help out.

CC-BY-NC 4.0 licensed, as found in the LICENSE file.

If you use this codebase, please cite us:

@misc{bose2025lorepersonalizingllmslowrank,
      title={LoRe: Personalizing LLMs via Low-Rank Reward Modeling}, 
      author={Avinandan Bose and Zhihan Xiong and Yuejie Chi and Simon Shaolei Du and Lin Xiao and Maryam Fazel},
      year={2025},
      eprint={2504.14439},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2504.14439}, 
}