Yuancheng Xu¹ ,  Udari Madhushani Sehwag² ,  Alec Koppel² ,  Sicheng Zhu¹ ,  Bang An¹ , 
Furong Huang¹ ,  Sumitra Ganesh^2  
1University of Maryland, College Park    ²JPMorgan AI Research

ICLR, 2025

Paper      Project Page

Overview: GenARM uses an autoregressive reward model to efficiently guide a base LLM for test-time alignment, outperforming prior methods and enabling weak-to-strong guidance and multi-objective alignment.

Our codebase is based on TRL for training and Model_Arithmetic for inference. Run the following to set up the environment:

conda deactivate
conda create -n genarm python=3.10
conda activate genarm
cd language-model-arithmetic/
python -m pip install -e .
cd ..
pip install -r requirements.txt

Also, in case if you haven't done so, set up your hugging face token and wandb

wandb login
python -c "from huggingface_hub.hf_api import HfFolder; HfFolder.save_token('MY_HUGGINGFACE_TOKEN_HERE')"

For training Tulu2 models, we use the open-instruct codebase. Please follow the README in the training_openInstruct/ folder to install the conda environment for training.

import torch
from transformers import AutoTokenizer
from model_arithmetic import ModelArithmetic, PromptedLLM
### set the base model, autoregressive reward model as well as the template
model_base = "allenai/tulu-2-7b"
model_autoregressive_rm = "YuanchengXu/AutoregressiveRM-tulu2-7b" # Your Autoregressive RM PATH
# this template is for Tulu2 
prompt_template = lambda system_prompt, input_string: f"<|user|>\n{input_string}\n<|assistant|>\n" 
### the parameters for GenARM (customized by the user)
alpha = 1
temperature = 1
### Initialize GenARM model through model arithmetic
tokenizer = AutoTokenizer.from_pretrained(model_base)
M_base = PromptedLLM(system_prompt="Not used for tulu2", prompt_template=prompt_template, model=model_base, tokenizer=tokenizer) 
M_arm = PromptedLLM(system_prompt="Not used for tulu2", prompt_template=prompt_template, model=model_autoregressive_rm, tokenizer=tokenizer) 
formula = M_base + alpha * M_arm
M = ModelArithmetic(formula, needs_input_tokens_lm_eval=False, lm_eval_task=None, dtype=torch.bfloat16) 
### Generate response!
prompt = "Tell me a short story about a dragon in a sentence."
response = M.generate_text(prompt, max_new_tokens=256, temperature=temperature/(1+alpha), top_p=1, top_k=0, do_speculation=False)[0].removesuffix(M.tokenizer.eos_token)
print(response)
# -> "Once upon a time, in a far-off kingdom, there was a mighty dragon that guarded a treasure trove of gold and jewels, fiercely protecting it from any who dared to approach."

To train a LLaMA based Autoregressive Reward Model on the hh-rlhf dataset using LoRA:

bash training_trl/train_arm_llama_HH.sh

You can also adapt the bash script for DPO training by setting algorithm=dpo.

For experiments on multi-objective alignment, we train Alpaca based Autoregressive Reward Model on the PKU-Alignment/PKU-SafeRLHF-10K dataset:

bash training_trl/train_arm_alpaca_SafeRLHF.sh

You need to set version in the bash script to specify training a harmlessness or helpfulness reward model. It also supports DPO training by setting algorithm=dpo.

Please follow the README in the training_openInstruct/ folder for training.

For the weak-to-strong experiments using 7B Autoregressive Reward Model to guide allenai/tulu-2-7b, allenai/tulu-2-13b, allenai/tulu-2-70b (or their GPTQ quantized version), we use the AlpacaEval 2 evaluation.

For environment installation, how to generate responses using GenARM and how to do head-to-head comparison between models' outputs, please refer to the README in the evaluation/ApacaEval2 folder.

For the experiments on aligning LLMs with general human preference using the HH-RLHF dataset, the evaluation instructions are in the README in the ./evaluation/HH folder.

For the experiments on aligning LLMs with two preference dimensions (helpfulness & harmlessness), the evaluation instructions are in the README in the ./evaluation/PKU-SafeRLHF folder.