RLHF-Reward-Modeling

Recipes to train reward model for RLHF.

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This repository, RLHF-Reward-Modeling, is dedicated to training reward models for DRL-based RLHF (PPO), Iterative SFT, and iterative DPO. It provides state-of-the-art performance in reward models with a base model size of up to 13B. The installation instructions involve setting up the environment and aligning the handbook. Dataset preparation requires preprocessing conversations into a standard format. The code can be run with Gemma-2b-it, and evaluation results can be obtained using provided datasets. The to-do list includes various reward models like Bradley-Terry, preference model, regression-based reward model, and multi-objective reward model. The repository is part of iterative rejection sampling fine-tuning and iterative DPO.

README:

RLHF-Reward-Modeling

Our models and codes have contributed to many academic research projects, e.g.,

Xu Zhangchen, et al. "Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing."
Chen, Lichang, et al. "OPTune: Efficient Online Preference Tuning."
Xie, Tengyang, et al. "Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF." arXiv preprint arXiv:2405.21046 (2024).
Zhong, Han, et al. "Dpo meets ppo: Reinforced token optimization for rlhf." arXiv preprint arXiv:2404.18922 (2024).
Zheng, Chujie, et al. "Weak-to-strong extrapolation expedites alignment." arXiv preprint arXiv:2404.16792 (2024).
Ye, Chenlu, et al. "A theoretical analysis of nash learning from human feedback under general kl-regularized preference." arXiv preprint arXiv:2402.07314 (2024).
Chen, Ruijun, et al. "Self-Evolution Fine-Tuning for Policy Optimization"
Li Bolian, et al., Cascade Reward Sampling for Efficient Decoding-Time Alignment
Zhang, Yuheng, et al. "Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning"
Lin Tzu-Han, et al., "DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging",
Yang Rui, et al., "Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs"
Junsoo Park, et al., "OffsetBias: Leveraging Debiased Data for Tuning Evaluators"
Meng Yu, et al., "SimPO: Simple Preference Optimization with a Reference-Free Reward"
Song Yifan, et al., "The Good, The Bad, and The Greedy: Evaluation of LLMs Should Not Ignore Non-Determinism"

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🚀 Our ArmoRM is the Rank #1 8B model on RewardBench!

🚀 The top-3 open-source 8B reward models on RewardBench (ArmoRM, Pair Pref. Model, BT RM) are all trained with this repo!

🚀 The pairwise preference model training code is available (pair-pm/)!

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Tech Report
- RLHF Workflow: From Reward Modeling to Online RLHF
- [ArmoRM] Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts
Models:
- Absolute-Rating Multi-Objective Reward Model (ArmoRM): ArmoRM-Llama3-8B-v0.1
- Pairwise Preference Reward Model: pair-preference-model-LLaMA3-8B
- Bradley-Terry Reward Model: FsfairX-LLaMA3-RM-v0.1
Architectures
- Bradley-Terry (BT) Reward Model and Pairwise Preference Model
- Absolute-Rating Multi-Objective Reward Model (ArmoRM)

RewardBench LeaderBoard

Model	Base Model	Method	Score	Chat	Chat Hard	Safety	Reasoning	Prior Sets (0.5 weight)
ArmoRM-Llama3-8B-v0.1 (Ours)	Llama-3 8B	ArmoRM + MoE	89.0	96.9	76.8	92.2	97.3	74.3
Cohere May 2024	Unknown	Unknown	88.2	96.4	71.3	92.7	97.7	78.2
pair-preference-model (Ours)	Llama-3 8B	SliC-HF	85.7	98.3	65.8	89.7	94.7	74.6
GPT-4 Turbo (0125 version)	GPT-4 Turbo	LLM-as-a-Judge	84.3	95.3	74.3	87.2	86.9	70.9
FsfairX-LLaMA3-RM-v0.1 (Ours)	Llama-3 8B	Bradley-Terry	83.6	99.4	65.1	87.8	86.4	74.9
Starling-RM-34B	Yi-34B	Bradley-Terry	81.4	96.9	57.2	88.2	88.5	71.4

Evaluation Results (from RLHF Workflow)

TL;DL: this is a repo for training the reward/preference model for DRL-based RLHF (PPO), Iterative SFT (Rejection sampling fine-tuning), and iterative DPO.

4 x A40 48G: we can train Gemma-7B-it with max_length 4096 by Deepspeed Zero-3 + gradient checkpoint;
4 x A100 80G: we can train Gemma-7B-it with max_length 4096 by gradient checkpoint;
The resulting reward models achieve SOTA performance as open-source RMs in the leaderboard of RewardBench.
Check out our blog post!

Installation instructions

It is recommeded to create separate environmnets for the Bradley-Terry reward model and pair wise preference model. The installation instructions are provided in the corresponding folders.

Dataset Preparation

The dataset should be preprocessed as the standard format, where each of the sample consists of two conversations 'chosen' and 'rejected' and they share the same prompt. Here is an example of the rejected sample in the comparison pair.

[
{ "content": "Please identify the top 5 rarest animals in the world.", "role": "user" },
{ "content": "Do you mean animals that are really rare, or rare relative to the size of the human population?", "role": "assistant" },
{ "content": "The ones that are really rare.", "role": "user" },
{ "content": "Alright, here’s what I found:", "role": "assistant" }, 
]

We preprocess many open-source preference datasets into the standard format and upload them to the hugginface hub. You can find them HERE. We have also searched and founda that some of the following mixture of preference dataset useful.

weqweasdas/preference_dataset_mix2
weqweasdas/preference_dataset_mixture2_and_safe_pku
hendrydong/preference_700K where the details can be found in the dataset card.

Evaluation Results

You can evaluate the resulting reward model with the dataset provided by benchmark by the following command.

CUDA_VISIBLE_DEVICES=1 python ./useful_code/eval_reward_bench_bt.py --reward_name_or_path ./models/gemma_2b_mixture2_last_checkpoint --record_dir ./bench_mark_eval.txt

To Do

[x] Bradley-Terry Reward Model
[x] Preference model
[x] Multi-Objective Reward Model
[ ] LLM-as-a-judge

Citation

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 citing:

@article{dong2024rlhf,
  title={RLHF Workflow: From Reward Modeling to Online RLHF},
  author={Dong, Hanze and Xiong, Wei and Pang, Bo and Wang, Haoxiang and Zhao, Han and Zhou, Yingbo and Jiang, Nan and Sahoo, Doyen and Xiong, Caiming and Zhang, Tong},
  journal={arXiv preprint arXiv:2405.07863},
  year={2024}
}

@article{ArmoRM,
      title={Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts}, 
      author={Haoxiang Wang and Wei Xiong and Tengyang Xie and Han Zhao and Tong Zhang},
      journal={arXiv preprint arXiv:2406.12845},
}


@article{dong2023raft,
  title={{RAFT}: Reward rAnked FineTuning for Generative Foundation Model Alignment},
  author={Hanze Dong and Wei Xiong and Deepanshu Goyal and Yihan Zhang and Winnie Chow and Rui Pan and Shizhe Diao and Jipeng Zhang and KaShun SHUM and Tong Zhang},
  journal={Transactions on Machine Learning Research},
  issn={2835-8856},
  year={2023},
  url={https://openreview.net/forum?id=m7p5O7zblY},
}

@article{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},
      journal={ICML}
}

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