metadata

license: llama3

Arbitrary-Rating Multi-Objective Reward Model (ArmoRM) with Mixture-of-Experts (MoE) Aggregation of Reward Objectives

Authors (* indicates equal contribution)

Haoxiang Wang*, Wei Xiong*, Tengyang Xie, Han Zhao, Tong Zhang
Blog: To appear soon (with implementation details)
Tech Report: To be released in June 2024
Model: ArmoRM-Llama3-8B-v0.1
- Finetuned from model: FsfairX-LLaMA3-RM-v0.1

Code Repository: https://github.com/RLHFlow/RLHF-Reward-Modeling/

Architecture

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	88.97	96.9	76.8	92.2	97.3	74.3
Cohere May 2024	Unknown	Unknown	88.25	96.4	71.3	92.7	97.7	78.2
GPT-4 Turbo (0125 version)	GPT-4 Turbo	LLM-as-a-Judge	84.25	95.3	74.3	87.2	86.9	70.9
FsfairX-LLaMA3-RM-v0.1	Llama-3 8B	Bradley-Terry	83.61	99.4	65.1	87.8	86.4	74.9
Starling-RM-34B	Yi-34B	Bradley-Terry	81.44	96.9	57.2	88.2	88.5	71.4

Demo Code

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", 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.