Reward Model Overview
The reward model is trained from the base model mistralai/Mistral-7B-Instruct-v0.2.
The training script is available at https://github.com/WeiXiongUST/RLHF-Reward-Modeling .
Also see a short blog for the training details (data mixture, parameters...): https://www.notion.so/Reward-Modeling-for-RLHF-abe03f9afdac42b9a5bee746844518d0
Model Details
If you have any question with this reward model and also any question about reward modeling, feel free to drop me an email with wx13@illinois.edu. I would be happy to chat!
Dataset preprocessing
The model is trained on a mixture of the following datasets. We also provide the mixture in weqweasdas/preference_dataset_mixture2_and_safe_pku.
Difference between this mixture and the original dataset
- HH-RLHF: we only use the helpful subset and we delete the noisy samples where chosen_response == rejected_response;
- SHP: we only use the samples with score ratio > 2, for each prompt, we take 5 comparison at most, leading to 109526;
- Ultrafeedback: similar to UltraFeedback-Binarized, we use the fine-grained score instead of the overall one to rank samples. Meanwhile, for each prompt, we take all possible 6 pairs of comparisons. Finally, we delete the selected pairs with equal scores, leading to 267416.
- HelpSteer: we use the mean of helpfulness and correctness to rank samples. Meanwhile, we take all possible 6 pairs of comparisons. Finally, we delete the selected pairs with equal scores, leading to 21576;
Training
We train the model for one epoch with a learning rate of 5e-6, batch size 512, cosine learning rate decay with a warmup ratio 0.03.
Uses
from transformers import AutoTokenizer, pipeline
rm_tokenizer = AutoTokenizer.from_pretrained("weqweasdas/RM-Mistral-7B")
device = 0 # accelerator.device
rm_pipe = pipeline(
"sentiment-analysis",
model="weqweasdas/RM-Mistral-7B",
#device="auto",
device=device,
tokenizer=rm_tokenizer,
model_kwargs={"torch_dtype": torch.bfloat16}
)
pipe_kwargs = {
"return_all_scores": True,
"function_to_apply": "none",
"batch_size": 1
}
chat = [
{"role": "user", "content": "Hello, how are you?"},
{"role": "assistant", "content": "I'm doing great. How can I help you today?"},
{"role": "user", "content": "I'd like to show off how chat templating works!"},
]
test_texts = [tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False).replace(tokenizer.bos_token, "")]
pipe_outputs = rm_pipe(test_texts, **pipe_kwargs)
rewards = [output[0]["score"] for output in pipe_outputs]
Results
The reward model ranks 2nd in the RewardBench
Reference
The repo was part of the iterative rejection sampling fine-tuning and iterative DPO. If you find the content of this repo useful in your work, please consider cite it as follows:
@article{dong2023raft,
title={Raft: Reward ranked finetuning for generative foundation model alignment},
author={Dong, Hanze and Xiong, Wei and Goyal, Deepanshu and Pan, Rui and Diao, Shizhe and Zhang, Jipeng and Shum, Kashun and Zhang, Tong},
journal={arXiv preprint arXiv:2304.06767},
year={2023}
}
@misc{xiong2024iterative,
title={Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint},
author={Wei Xiong and Hanze Dong and Chenlu Ye and Ziqi Wang and Han Zhong and Heng Ji and Nan Jiang and Tong Zhang},
year={2024},
eprint={2312.11456},
archivePrefix={arXiv},
primaryClass={cs.LG}
}