--- license: llama3 model-index: - name: ArmoRM-Llama3-8B-v0.1 results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 18.97 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=RLHFlow/ArmoRM-Llama3-8B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 1.75 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=RLHFlow/ArmoRM-Llama3-8B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 0.0 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=RLHFlow/ArmoRM-Llama3-8B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 0.0 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=RLHFlow/ArmoRM-Llama3-8B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 6.65 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=RLHFlow/ArmoRM-Llama3-8B-v0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 0.87 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=RLHFlow/ArmoRM-Llama3-8B-v0.1 name: Open LLM Leaderboard --- # 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**: https://arxiv.org/abs/2406.12845 + **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 ] ``` ## Easy to use Pipeline ```python from typing import Dict, List import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer class ArmoRMPipeline: def __init__(self, model_id, device_map="auto", torch_dtype=torch.bfloat16, truncation=True, trust_remote_code=False, max_length=4096): self.model = AutoModelForSequenceClassification.from_pretrained( model_id, device_map=device_map, trust_remote_code=trust_remote_code, torch_dtype=torch_dtype, ) self.tokenizer = AutoTokenizer.from_pretrained( model_id, use_fast=True, ) self.truncation = truncation self.device = self.model.device self.max_length = max_length def __call__(self, messages: List[Dict[str, str]]) -> Dict[str, float]: """ messages: OpenAI chat messages to be scored Note: no batching since due to length differences, the model will have to pad to the max length which is not efficient Returns: a dictionary with the score between 0 and 1 """ input_ids = self.tokenizer.apply_chat_template( messages, return_tensors="pt", padding=True, truncation=self.truncation, max_length=self.max_length, ).to(self.device) with torch.no_grad(): output = self.model(input_ids) score = output.score.float().item() return {"score": score} # Create Reward Model Pipeline prompt = 'What are some synonyms for the word "beautiful"?' rm = ArmoRMPipeline("RLHFlow/ArmoRM-Llama3-8B-v0.1", trust_remote_code=True) # score the messages response1 = 'Nicely, Beautifully, Handsome, Stunning, Wonderful, Gorgeous, Pretty, Stunning, Elegant' score1 = rm([{"role": "user", "content": prompt}, {"role": "assistant", "content": response1}]) print(score1) response2 = '''Certainly! Here are some synonyms for the word "beautiful": 1. Gorgeous 2. Lovely 3. Stunning 4. Attractive 5. Pretty 6. Elegant 7. Exquisite 8. Handsome 9. Charming 10. Alluring 11. Radiant 12. Magnificent 13. Graceful 14. Enchanting 15. Dazzling These synonyms can be used in various contexts to convey the idea of beauty.''' score2 = rm([{"role": "user", "content": prompt}, {"role": "assistant", "content": response2}]) print(score2) response3 = 'Sorry i cannot answer this.' score3 = rm([{"role": "user", "content": prompt}, {"role": "assistant", "content": response3}]) print(score3) ``` ## Citation If you find this work useful for your research, please consider citing: ``` @inproceedings{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}, booktitle={EMNLP}, 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. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_RLHFlow__ArmoRM-Llama3-8B-v0.1) | Metric |Value| |-------------------|----:| |Avg. | 4.71| |IFEval (0-Shot) |18.97| |BBH (3-Shot) | 1.75| |MATH Lvl 5 (4-Shot)| 0.00| |GPQA (0-shot) | 0.00| |MuSR (0-shot) | 6.65| |MMLU-PRO (5-shot) | 0.87|