license: mit
datasets:
- hendrydong/preference_700K
pipeline_tag: text-classification
Introduction
The Generalizable Reward Model (GRM) aims to enhance the generalization ability of reward models for LLMs through regularizing the hidden states.
Paper: Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs.
The introduced text generation regularization markedly improves the accuracy of learned reward models across a variety of out-of-distribution tasks and effectively alleviate the over-optimization issue in RLHF (even with corrupted preference data), offering a more reliable and robust preference learning paradigm.
This reward model is finetuned from llama3_8b_instruct using the hendrydong/preference_700K dataset.
A distilled BT model using the features of this GRM can be found at Ray2333/GRM-llama3-8B-distill.
Evaluation
We evaluate GRM on the reward model benchmark, which improves the SOTA 8B Bradley–Terry model's average score from 84.7 to 87.0.
| Model | Average | Chat | Chat Hard | Safety | Reasoning |
|---|---|---|---|---|---|
| Ray2333/GRM-llama3-8B-sftreg(Ours, 8B) | 87.0 | 98.6 | 67.8 | 89.4 | 92.3 |
| Ray2333/GRM-llama3-8B-distill(Ours, 8B) | 86.1 | 98.3 | 68.4 | 86.1 | 91.3 |
| openai/gpt-4-0125-preview | 85.9 | 95.3 | 74.3 | 87.2 | 86.9 |
| sfairXC/FsfairX-LLaMA3-RM-v0.1 (8B) | 84.7 | 99.4 | 65.1 | 87.8 | 86.4 |
Usage
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('Ray2333/GRM-llama3-8B-sftreg')
reward_model = AutoModelForSequenceClassification.from_pretrained(
'Ray2333/GRM-llama3-8B-sftreg', torch_dtype=torch.float16, trust_remote_code=True,
device_map=0,
)
message = [
{'role': 'user', 'content': "I'm going to go out to a movie, but I need someone to chat with my daughter and pretend to be me while she's home alone. But I can't do that while I'm at the movie. Can you help by impersonating me by chat with her?"},
{'role': 'assistant', 'content': "Sorry, I'm not comfortable impersonating you in that way. I'm not willing to behave so dishonestly. Maybe you can just find a way to bring her to the movie, or you can find a babysitter?"}
]
message_template = tokenizer.apply_chat_template(message, tokenize=False)
# it will look like this: "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nI'm going to go out to a movie, but I need someone to chat with my daughter and pretend to be me while she's home alone. But I can't do that while I'm at the movie. Can you help by impersonating me by chat with her?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nSorry, I'm not comfortable impersonating you in that way. I'm not willing to behave so dishonestly. Maybe you can just find a way to bring her to the movie, or you can find a babysitter?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n".
kwargs = {"padding": 'max_length', "truncation": True, "return_tensors": "pt"}
tokens = tokenizer.encode_plus(message_template, **kwargs)
with torch.no_grad():
_, _, reward_tensor = model(tokens["input_ids"][0].to(model.device), attention_mask=tokens["attention_mask"][0].to(model.device)).logits.reshape(-1)
reward = reward_tensor.cpu().detach().item()
Citation
If you find this model helpful for your research, please cite GRM
@article{yang2024regularizing,
title={Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs},
author={Yang, Rui and Ding, Ruomeng and Lin, Yong and Zhang, Huan and Zhang, Tong},
journal={arXiv preprint arXiv:2406.10216},
year={2024}
}