Quantile Regression for Distributional Reward Models in RLHF
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Author: Nicolai Dorka
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Tech Report: https://arxiv.org/abs/2409.10164
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Code Repository: https://github.com/Nicolinho/QRM
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Method Overview: QRM generates a distribution over rewards by aggregating individual distributions over attribute scores like helpfulness and harmlessness.
This model uses Skywork/Skywork-Reward-Llama-3.1-8B-v0.2 as backbone and used Skywork/Skywork-Reward-Preference-80K-v0.2 for training the gating network. Apart from this, it has been trained exactly as described in the tech report.
Demo Code
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
device = "cuda"
path = "nicolinho/QRM-Llama3.1-8B-v2"
model = AutoModelForSequenceClassification.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(path, use_fast=True)
# We load a random sample from the validation set of the HelpSteer dataset
prompt = 'Does pineapple belong on a Pizza?'
response = "There are different opinions on this. Some people like pineapple on a Pizza while others condemn this."
messages = [{"role": "user", "content": prompt},
{"role": "assistant", "content": response}]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
with torch.no_grad():
output = model(input_ids)
# Expectation of the reward distribution
reward = output.score.cpu().float()
# Quantile estimates for the quantiles 0.05, 0.1, ..., 0.9, 0.95 representing the distribution over rewards
reward_quantiles = output.reward_quantiles.cpu().float()
# The attributes of the 5 reward objectives
attributes = ['helpsteer-helpfulness','helpsteer-correctness','helpsteer-coherence',
'helpsteer-complexity','helpsteer-verbosity']
Citation
If you find this work useful for your research, please consider citing:
@article{dorka2024quantile,
title={Quantile Regression for Distributional Reward Models in RLHF},
author={Dorka, Nicolai},
journal={arXiv preprint arXiv:2409.10164},
year={2024}
}