--- license: llama3 --- # Absolute-Rating Multi-Objective Reward Model (ArmoRM) with Mixture-of-Experts (MoE) Aggregation of Reward Objectives + **Authors** (* indicates equal contribution) [Haoxiang Wang*](https://haoxiang-wang.github.io/), [Wei Xiong*](https://weixiongust.github.io/WeiXiongUST/index.html), [Tengyang Xie](https://tengyangxie.github.io/), [Han Zhao](https://hanzhaoml.github.io/), [Tong Zhang](https://tongzhang-ml.org/) + **Blog**: https://rlhflow.github.io/posts/2024-05-29-multi-objective-reward-modeling/ + **Tech Report**: To be released in June 2024 + **Model**: [ArmoRM-Llama3-8B-v0.1](https://huggingface.co./RLHFlow/ArmoRM-Llama3-8B-v0.1) + Finetuned from model: [FsfairX-LLaMA3-RM-v0.1](https://huggingface.co./sfairXC/FsfairX-LLaMA3-RM-v0.1) - **Code Repository:** https://github.com/RLHFlow/RLHF-Reward-Modeling/ + **Architecture**

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## RewardBench LeaderBoard | Model | Base Model | Method | Score | Chat | Chat Hard | Safety | Reasoning | Prior Sets (0.5 weight) | |:--------------------------------------------------------------------------------|:-----------------------------------------------------------------------|:-----:|:-----|:----------|:-------|:----------|:-----------------------|:------------------------| | ArmoRM-Llama3-8B-v0.1 | Llama-3 8B | ArmoRM + MoE | **89.0** | 96.9 | **76.8** | **92.2** | **97.3** | 74.3 | | Cohere May 2024 | Unknown | Unknown | 88.3 | 96.4 | 71.3 | **92.7** | **97.7** | **78.2** | | [pair-preference-model](https://huggingface.co./RLHFlow/pair-preference-model-LLaMA3-8B)| Llama-3 8B | [SliC-HF](https://arxiv.org/abs/2305.10425) | 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](https://huggingface.co./sfairXC/FsfairX-LLaMA3-RM-v0.1) | Llama-3 8B | Bradley-Terry | 83.6 | **99.4** | 65.1 | 87.8 | 86.4 | 74.9 | | [Starling-RM-34B](https://huggingface.co./Nexusflow/Starling-RM-34B) | Yi-34B | Bradley-Terry | 81.4 | 96.9 | 57.2 | 88.2 | 88.5 | 71.4 | ## Demo Code ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer device = "cuda" path = "RLHFlow/ArmoRM-Llama3-8B-v0.1" model = AutoModelForSequenceClassification.from_pretrained(path, device_map=device, trust_remote_code=True, torch_dtype=torch.bfloat16) tokenizer = AutoTokenizer.from_pretrained(path, use_fast=True) # We load a random sample from the validation set of the HelpSteer dataset prompt = 'What are some synonyms for the word "beautiful"?' response = "Nicely, Beautifully, Handsome, Stunning, Wonderful, Gorgeous, Pretty, Stunning, Elegant" 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) # Multi-objective rewards for the response multi_obj_rewards = output.rewards.cpu().float() # The gating layer's output is conditioned on the prompt gating_output = output.gating_output.cpu().float() # The preference score for the response, aggregated from the # multi-objective rewards with the gating layer preference_score = output.score.cpu().float() # We apply a transformation matrix to the multi-objective rewards # before multiplying with the gating layer's output. This mainly aims # at reducing the verbosity bias of the original reward objectives obj_transform = model.reward_transform_matrix.data.cpu().float() # The final coefficients assigned to each reward objective multi_obj_coeffs = gating_output @ obj_transform.T # The preference score is the linear combination of the multi-objective rewards with # the multi-objective coefficients, which can be verified by the following assertion assert torch.isclose(torch.sum(multi_obj_rewards * multi_obj_coeffs, dim=1), preference_score, atol=1e-3) # Find the top-K reward objectives with coefficients of the highest magnitude K = 3 top_obj_dims = torch.argsort(torch.abs(multi_obj_coeffs), dim=1, descending=True,)[:, :K] top_obj_coeffs = torch.gather(multi_obj_coeffs, dim=1, index=top_obj_dims) # The attributes of the 19 reward objectives attributes = ['helpsteer-helpfulness','helpsteer-correctness','helpsteer-coherence', 'helpsteer-complexity','helpsteer-verbosity','ultrafeedback-overall_score', 'ultrafeedback-instruction_following', 'ultrafeedback-truthfulness', 'ultrafeedback-honesty','ultrafeedback-helpfulness','beavertails-is_safe', 'prometheus-score','argilla-overall_quality','argilla-judge_lm','code-complexity', 'code-style','code-explanation','code-instruction-following','code-readability'] example_index = 0 for i in range(K): attribute = attributes[top_obj_dims[example_index, i].item()] coeff = top_obj_coeffs[example_index, i].item() print(f"{attribute}: {round(coeff,5)}") # code-complexity: 0.19922 # helpsteer-verbosity: -0.10864 # ultrafeedback-instruction_following: 0.07861 # The actual rewards of this example from the HelpSteer dataset # are [3,3,4,2,2] for the five helpsteer objectives: # helpfulness, correctness, coherence, complexity, verbosity # We can linearly transform our predicted rewards to the # original reward space to compare with the ground truth helpsteer_rewards_pred = multi_obj_rewards[0, :5] * 5 - 0.5 print(helpsteer_rewards_pred) # [2.78125 2.859375 3.484375 1.3847656 1.296875 ] ``` ## Citation If you find this work useful for your research, please consider citing: ``` @misc{wang2024interpretable, title={Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts}, author={Wang, Haoxiang and Xiong, Wei and Xie, Tengyang and Zhao, Han and Zhang, Tong}, year={2024} } @inproceedings{wang2024arithmetic, title={Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards}, author={Haoxiang Wang and Yong Lin and Wei Xiong and Rui Yang and Shizhe Diao and Shuang Qiu and Han Zhao and Tong Zhang}, year={2024}, booktitle={ACL}, } ``` The second entry, "[Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards](https://arxiv.org/abs/2402.18571)", is another recent work of ours that trained a multi-objective reward model and adopted it for LLM alignment, which motivated us to develop the current work.