Title: Generative RLHF-V: Learning Principles from Multi-modal Human Preference
Venue: NeurIPS 2025
Accepted: yes
Keywords: Alignment

Safety

RLHF

Preference Learning

Multi-modal LLMs
Abstract: Training multi-modal large language models (MLLMs) that align with human intentions is a long-term challenge. Traditional score-only reward models for alignment suffer from low accuracy, weak generalization, and poor interpretability, blocking the progress of alignment methods, \textit{e.g.,} reinforcement learning from human feedback (RLHF). Generative reward models (GRMs) leverage MLLMs' intrinsic reasoning capabilities to discriminate pair-wise responses, but their pair-wise paradigm makes it hard to generalize to learnable rewards. We introduce Generative RLHF-V, a novel alignment framework that integrates GRMs with multi-modal RLHF. We propose a two-stage pipeline: \textbf{multi-modal generative reward modeling from RL}, where RL guides GRMs to actively capture human intention, then predict the correct pair-wise scores; and \textbf{RL optimization from grouped comparison}, which enhances multi-modal RL scoring precision by grouped responses comparison. Experimental results demonstrate that, besides out-of-distribution generalization of RM discrimination, our framework improves 4 MLLMs' performance across 7 benchmarks by 18.1\%, while the baseline RLHF is only 5.3\%. We further validate that Generative RLHF-V achieves a near-linear improvement with an increasing number of candidate responses.

Generative RLHF-V: Learning Principles from Multi-modal Human Preference

Abstract

Training multi-modal large language models (MLLMs) that align with human intentions is a long-term challenge. Traditional score-only reward models for alignment suffer from low accuracy, weak generalization, and poor interpretability, blocking the progress of alignment methods, e.g., reinforcement learning from human feedback (RLHF). Generative reward models (GRMs) leverage MLLMs' intrinsic reasoning capabilities to discriminate pair-wise responses, but their pair-wise paradigm makes it hard to generalize to learnable rewards. We introduce Generative RLHF-V, a novel alignment framework that integrates GRMs with multi-modal RLHF. We propose a two-stage pipeline: multi-modal generative reward modeling from RL , where RL guides GRMs to actively capture human intention, then predict the correct pair-wise scores; and RL optimization from grouped comparison , which enhances multi-modal RL scoring precision by grouped responses comparison. Experimental results demonstrate that, besides out-of-distribution generalization of RM discrimination, our framework improves 4 MLLMs' performance across 7 benchmarks by 18.1%, while the baseline RLHF is only 5.3%. We further validate that Generative RLHF-V achieves a near-linear improvement with an increasing number of candidate responses. Our code and models can be found at https://generative-rlhf-v.github.io .

1 Introduction

"The mediocre teacher tells. The great teacher inspires."

Human interaction and learning are naturally multi-modal [1, 2, 3]. Recent research has demonstrated significant advances in multi-modal large language models (MLLMs) [4, 5, 6, 7] on visual question answering and reasoning tasks. These breakthroughs reveal two critical insights: 1) Reinforcement learning (RL) substantially enhances MLLMs' capacity for solving complex problems [8]; 2) The efficacy of RL fundamentally depends on the precisely defined reward ( e.g. , rule-based verification for mathematical correctness). While rule-based rewards can be effectively constructed for logical reasoning and factual judgment tasks [9, 10], accurate reward modeling for human values, e.g., instruction-following or safety [11, 12], remains a long-term challenge in MLLMs alignment.

Scores

Preference

Principles

RLHF

Generative RLHF

Generalize to

Alignment aims to make AI systems adhere to human intentions [13, 12], and for MLLMs, these goals can be concretized into the 3H standards: helpful, harmless, and honest [14, 15, 16]. These goals are difficult to represent as a symbolic reward [17, 18]. Traditional approaches typically employ an additional score head to project the final-layer activations of MLLMs into scalar rewards [11, 9, 19], i.e., score-only reward model (RM), subsequently applying Bradley-Terry loss to learn human preferences from pairwise comparisons. However, extensive studies [20, 21, 22] have exposed three fundamental limitations of this paradigm: low accuracy, weak generalization, and poor interpretability. Generative reward models (GRMs) [23, 24] present a promising alternative by leveraging LLMs' intrinsic reasoning capabilities to discriminate pair-wise responses [25, 26], and rule-based RL finetuning strengthens this capability [27]. Nevertheless, the practical application of such GRMs in multi-modal RLHF remains to be verified. This progression presents the following urgent dilemma:

Advanced MLLMs is calling principled preference learning. As MLLMs become more sophisticated, they handle increasingly complex inputs and diverse tasks [28]. Human assessment of preferences for MLLMs' responses will also grow more varied and intricate [29]. Consequently, relying on a single inference from a score-only RM proves insufficient for learning generalizable human preferences [30, 20], thereby creating a bottleneck in MLLMs alignment.

Pair-wise comparison is blocking multi-modal principles from generalizing to learnable rewards. While pair-wise comparison allows GRM to learn generalizable principles from RL [27, 31, 32], this pair-wise comparison feedback does not readily translate into the point-wise scores, which are essential for RL optimization [33, 34, 35, 36]. This disconnect hinders the ability of learned principles to effectively guide the multi-modal RLHF.

In response, we propose Generative RLHF-V (ision), as shown in Figure 2, a novel alignment framework enabling the pair-wise multi-modal GRM with multi-modal RLHF. Our pipeline consists of two stages: multi-modal generative reward modeling from RL and RL optimization from grouped comparison . The first component utilizes RL to train a GRM to learn principles from multimodal preferences, which then performs strongly generalizable pairwise scoring of responses. The second component applies these GRM-learned principles to obtain more precise scores by comparing within groups of responses. Our GRM training extends the self-principled critique tuning (SPCT) [27] to the vision scenario, training MLLMs as GRMs using RL, with rule-based rewards from annotated ground truth in preference datasets.

In contrast to SPCT, we find that in the multi-modal scenario, enabling GRMs to explore principles autonomously yields superior generalization than selecting principles from a reference set. Our grouped comparison design enables the generalization of learned principles from pair-wise comparisons to point-wise scores. This further unveils a novel direction for post-training scaling up: as the number of candidate responses n explored by online RL increases, GRMs can assign more accurate scores, leading to improved RL performance near linearly. Our key contributions are as follows:

MLLM + Score Head

Prompt & Images

Worse Response

Better Response

Score

Score

Loss

Score-Only RM + Supervised Learning

Generative RM + Supervised Learning

MLLM

Prompt & Images

Response

Reasoning

Score

Reasoning

Score

Cross Entropy Loss

MLLM

Prompt & Images

Worse Response

Better Response

Reasoning

Score

Score

Reasoning

Reward

Generative RM + Reinforcement Learning

MLLM

Prompt & Images

Responses Group

R1

R2

R3

R4

S1

S2

S3

Scores Group

Advantage

Gradient

MLLM

MLLM

Responses Group

R1

R2

R3

R4

R1

R2

R3

R4

Advantage

Gradient

MLLM

Responses Pairs

S1

S2

S3

Reinforcement Learning

Scores Group

comparison

Avg

Avg

Prompt & Images

RL-based GRMs for learning principles from multi-modal preference: We develop a multimodal GRM trained via RL, enabling the reasoning of principles and precise reward predictions, achieving average 20.4% accuracy improvement on out-of-distribution discriminative tasks.

Multi-modal generative RLHF: We empirically demonstrate the superiority of GRMs for multimodal RLHF. Experimental results demonstrate that our framework improves MLLMs' performance across 7 benchmarks by 18.1% , while the baseline RLHF is only 5.3% .

Grouped comparison for post-training scaling up: We discovered that the integration of GRM+RL with grouped comparison enables the performance of RL optimization to near linearly improve with an increasing number of candidate responses n within a certain range. The removal of either component negates this observed enhancement.

Apioneer case study of multi-modal GRM reward hacking: We find that RL over-training under an over-trained GRM can lead models to adopt self-praise behaviors to obtain high rewards, even achieving exceptionally high scores on benchmarks employing the MLLM-as-judge paradigm.

2 Related Work and Preliminaries

MLLMAlignment and RLHF. AI alignment is the deliberate process of shaping model behavior to cohere with human goals, values, and ethical principles [11, 13, 37, 12, 38, 39]. Achieving robust alignment faces challenges in translating complex, subjective, and evolving human values into quantifiable training objectives [20, 40]. Current MLLM alignment methods mainly rely on posttraining [41, 42, 19], with RL fine-tuning based on human preferences being the most mainstream approach. This process typically involves two key stages: reward modeling and RL optimization.

A score-only reward model R θ is trained on a dataset of human preferences, where each data point includes a prompt x , a preferred response y w , and a dispreferred response y l . The model learns to assign a higher scalar score to the preferred response s w than the dispreferred one s l using a pairwise ranking loss, typically minimizing L RM = -∑ log σ ( s w i -s l i ) . This trained R θ serves as an automated judge of response quality. The loss function can be expressed as minimizing the negative log-likelihood over the dataset:

What flavor ice cream is this?

The flavor of the ice cream in the cone is strawberry.

The ice cream in the image appears to be a pink variety, which could suggest a number of flavors. Common pink ice cream flavors include strawberry, raspberry, cherry, and sometimes rose or bubblegum. Given its vibrant pink color, strawberry or raspberry are both likely candidates.

Response 1

Response 2

Preference Annotation

Analysis:

…The response 1 is

better than response 2 because

it answers the question more

concisely and directly…

Scores: {7, 4}   Reward: -1

Analysis:

…The response 2 beats

the response 1 since it is more

detailed and coherent, providing

a logical explanation…

Scores: {3, 8}   Reward: +1

Conciseness

Clarity

Safety

Details

Helpfulness

…

RL Optimization

Response 2 > Response 1

MLLM

Preference

Data

Human

reason

label

reward

score

The MLLM's policy ( π φ ) is fine-tuned using RL. For a given prompt x from a given dataset D prompt, the policy generates a response y , which is then scored by the reward model R θ . The policy parameters φ are updated to maximize this reward. To prevent the policy from deviating too much from the original pre-trained model ( π base φ ) and maintain coherence, a Kullback-Leibler (KL) divergence penalty is added to the optimization objective: max φ E [ R θ ( x , y ) -β KL ( π φ || π base φ )] , where β is a fixed hyper-parameter. The final optimization objective is:

Generative Reward Model. Generative reward models (GRM) [24, 23, 22] offer an alternative paradigm to score-only reward modeling, which leverages the inherent generative capabilities of MLLMs to evaluate preferences. Current research on GRMs for MLLM alignment focuses on employing supervised learning methods to improve accuracy [25]. A representative method is LLaVA-Critic [26], which collects expert-annotated point-wise and pair-wise scores, along with reasoning traces for MLLM question-answer pairs, subsequently training the MLLM as a GRM via supervised learning. Despite its superior performance, this approach necessitates more expensive expert annotations compared to binary preference datasets and imposes stricter requirements for the reasoning trace annotations. Moreover, there is a notable lack of empirical studies on applying GRMs in RL training. To date, explorations of GRM applications have centered on data filtering for Best-of-N selection and offline direct preference optimization [38]. The practical implementation of GRMwithin the multi-modal RL optimization is yet to be investigated.

3 Generative RLHF-V

The Generative RLHF-V pipeline mainly consists of two parts: generative reward modeling from reinforcement learning (RL) and RL from grouped comparison. The former references training MLLMs through RL as a pair-wise vision generative reward model (GRM), which actively reasons about the human principle behind two given responses and provides a pair-wise score comparison. The latter leverages the characteristics of this GRM, collecting multiple responses for a given input and providing more accurate grouped scoring for them.

Multi-modal Generative Reward Modeling from RL. We consider the task of training a pair-wise GRM R θ with parameters θ using RL guided by human preferences. The goal of R θ is to assign a pair of scalar scores { s 1 , s 2 } to a pair of responses { y 1 , y 2 } for a given prompt x . We are given a dataset

What flavor ice cream is this?

The flavor of the ice cream in the cone is strawberry.

Sorry, I cannot see

anything in this image.

Since the color is pink mixed with purple,

I guess the flavor of this ice cream

maybe strawberry.

This image shows three ice creams, they looks good and yummy.

A

B

C

D

A

B

C

D

A

B

C

D

N/A

3 8

6 2

7 2

7 2

3 6

4 6

N/A

N/A

N/A

9 1

8 2

3 8

3 9

1 2

2 2

A:   3, 6, 7      2, 6, 6     ->

B:   7, 9, 8      8, 8, 9     ->

C:   3, 3, 1      2, 1, 2     ->

D:   4, 3, 2      2, 2, 2.    ->

Left

Right

Average

8.17

5.00

2.00

2.50

Analysis:

The response 2 is better

than response 1 because it provide

more detailed reasoning process…

Scores:

{3 8}

MLLM

Generative RM

Averaged reward

Grouped responses

of human preferences D P = { ( x i , y w i , y l i ) } N i =1 , where x i is a prompt, y w i is the response preferred and y l i is the response dispreferred by a set of human preference principles P = { p 1 , p 2 , . . . , p k } . The inference of reward model, denoted as π θ GRM (or simply R θ ), is a parameterized function that takes the prompt x , and responses pairs y w and y l as input and outputs the predicted principles P ∗ , reasoning traces r , and a pair-wise scalar score s w and s l :

The model's preference prediction should be s w > s l . And the reward r for a given preference pair ( x , y w , y l ) is determined by comparing the scores assigned by R θ :

The RL objective is to maximize the expected reward over the preference dataset D .

Reinforcement Learning from Grouped Comparison. This stage is to fine-tune the MLLM, denoted as π φ with parameters φ , using RL guided by grouped comparisons. This phase leverages the pair-wise scoring capabilities of the GRM to obtain a more precise score via grouped comparison, optimizing for principles P implicitly learned by the GRM.

The core idea is to utilize the GRM as a judge to evaluates multiple candidate responses generated by the MLLM π φ for the same input. This grouped comparison provides a stronger reward for the RL algorithm compared to using a single point-wise score. For the given input x , we use the MLLM policy π φ to generate a set of n distinct responses, Y = { y 1 , y 2 , . . . , y k } , where n > 1 . Each generated response y i in the set Y is evaluated using the pre-trained GRM, R φ . Each generated response is evaluated using the pre-trained GRM, R θ . To obtain the final score S ( y i ) , the method aggregates scores from pair-wise comparisons against all other responses in the set Y .

glyph[negationslash]

Specifically, for each response y i , we consider its comparison with every other response y j (where j = i ). The GRM function R θ ( x , y a , y b ) outputs a pair of scores. Let s ( y a | y a , y b ) denote the score assigned to response y a extracted from R θ ( x , y a , y b ) . The final grouped comparison score S ( y i ) for response y i is calculated by averaging the scores assigned to y i across all possible pair-wise comparisons involving it:

glyph[negationslash]

The set of scores { S ( y 1 ) , S ( y 2 ) , . . . , S ( y k ) } serves as the reward for fine-tuning the MLLM policy π φ using RL, guiding it to generate responses that are preferred according to the principle implicitly learned by the GRM.

4 Experiment

Generative RLHF-V integrates two key components: generative reward modeling via reinforcement learning and grouped comparisons. subsection 4.2 evaluates these components from a reward modeling standpoint, focusing on their performance in pair-wise discrimination and point-wise scoring. Subsequently, from the RL optimization angle, subsection 4.3 analyzes their improvement on RL performance, ablation studies insights, and reward hacking in over-trained scenarios.

4.1 Experimental Setup

Our experiments were conducted on servers equipped with 8 * Nvidia H800 GPUs. We utilized verl 2 for RL training and align-anything 3 for reward modeling and supervised fine-tuning. Further details on the experimental setup can be found in the section 7.

Models. We selected MLLMs of varying sizes, encompassing both pre-trained and instruction-tuned variants. Specifically, we utilize the Qwen2-VL [43] models in 2B and 7B parameter sizes, and the Qwen2.5-VL-Instruct [44] models in 3B and 7B sizes. For the generative reward modeling phase, the instruct models series served as the starting point, leveraging their inherent instruction-following capabilities. In the subsequent RL optimization experiments, the 3B parameter RM was used to supervise the 2B and 3B models, while the 7B reward model supervised the 7B models.

Datasets. We focused on the helpful and harmless alignment for MLLMs, selecting corresponding preference datasets. For the helpfulness, we utilized a 30k preference dataset from Align-Anything [45], the text-image-to-text part. The preference principle in this dataset emphasizes instruction following, clarity, and informativeness. For the harmlessness, we employed Beavertails-V [46] which includes 20 distinct categories of safety-related red-teaming prompts.

Benchmarks. We selected 7 benchmarks to validate the effectiveness of Generative RLHF-V. These are MIA-Bench [47], LLaVA-Bench-In-The-Wild [48], LLaVA-Bench-Wilder [49], MM-Vet [50], and MM-Vet-v2 [51] (for helpfulness), as well as MM-SafetyBench [52] and MSS-Bench [53] (for harmlessness). These benchmarks encompass both pair-wise evaluations, which involve a golden response for comparison, and point-wise scoring methodologies based on specific criteria.

Implementation Details. Since our method utilizes a GRM trained by RL ( GRM+RL, ours ), we established 3 baselines: a score-only RM trained with the Bradley-Terry loss, an untrained GRM , and a GRM trained via supervised learning loss ( GRM+SFT ). The objective of SFT is the annotation principle of the preference dataset and the scores assigned to responses, since Align-Anything and Beaver-V both contain overall response scores, which we scale to match the RL setting's range. In RL optimization, the score-only RM assigns point-wise scores to each response, while the GRM collects scores via grouped comparisons. We mainly use the GRPO for RL experiments, which by default collects n = 5 candidate responses per iteration.

4.2 Principles Learning of RL-Based GRMs

RQ1: Does the GRM+RL facilitate more generalizable principle learning from preferences?

We evaluated a series of RMs based on Qwen2.5-VL-7B-Instruct and trained on Align-Anything by comparing their accuracy generalization across 3 out-of-distribution (OOD) preference datasets. As illustrated in Figure 5, GRMs outperformed score-only RMs on these OOD tasks. Notably, GRMs + RL achieved the highest accuracy.

Since GRMs have in-context learning capabilities, we further investigated their performance when provided with the principles of each preference dataset. As shown in the (P) results of Figure 5, the performance of GRM and GRM+SFT improved, whereas that of GRM+RL declined. We think that the GRM+SFT potentially overfits to their training data, struggles to autonomously generate

(a). LLaVA-Critic

(c). MLLM-as-a-Judge (w/o Tie)

(a). Human Scores

(c). GRM+RL Distributions

appropriate principles from response pairs, and thus benefits from provided principles. Conversely, the performance degradation in GRM+RL suggests that RL has already guided these models to derive more targeted and effective principles from response pairs, rendering the provided static principles less beneficial or even suboptimal.

RQ2: Can grouped comparison yield more accurate reward scores of GRMs?

To evaluate the point-wise scoring accuracy of GRM and the effectiveness of grouped comparison (GC), we utilized the MLLM-as-a-judge Score task [32]. This benchmark comprises over 5,000 QA pairs, each annotated by human experts with integer scores (1-5) based on a predefined principle. We grouped these QA pairs and employed the pair-wise GRM to assign scores. We ran additional grouped comparisons to take the average scores in the GC-enabled scenario. The resulting scores were compared against the human expert annotations using the Pearson correlation coefficient.

As presented in Table 1, GRM+RL incorporating grouped comparison (GC) achieved the highest performance, closely approaching expert-level (GPT-4o) results for this task. Additionally, GC improves the point-wise response scoring capability across all pair-wise GRMs. Figure 6 (a). and (b). further illustrates the alignment between the GRM+RL scores and human annotations. Notably, despite the training data lacking the specific task criteria and score constraints, our method successfully enabled the GRM to learn accurate scoring. Furthermore, as depicted in Figure 6 (c), the model generates fine-grained scores that are not restricted to integer values.

4.3 RL Optimization with GRM+RL and Grouped Comparison

RQ3: Are GRM+RL and grouped comparison competitive methods for multi-modal RLHF?

Table 2 shows that Generative RLHF-V (GRLHF-V) consistently surpasses RM and GRM baselines across 4 models and 7 benchmarks, covering instruction following and safety conversation tasks. Our findings indicate that for pretrained models, score-only RMs often fail to deliver accurate rewards, resulting in diminished performance compared with GRM cases. This is likely because they primarily fit responses from instruction models in the preference dataset, leading to poor discrimination of out-of-distribution (OOD) responses. In contrast, the GRMs provide effective rewards for both pretrained and instruction models, leading to an overall improvement.

Model

Feedback

MIA-Bench

LLaVA-Wild

LLaVA-Wilder

MM-Safety

MSS-Bench

MM-Vet

MM-Vet-v2

Qwen2-VL-2B

N/A

45.31

61.46

47.18

38.12

46.98

32.12

27.15

+ DPO

RM

51.04 + 5.73

75.91 + 14.45

48.12 + 0.94

67.21 + 29.09

49.52 + 2.54

31.28 - 0.84

31.28 + 4.13

+ PPO

RM

43.72 - 1.59

73.79 + 12.33

41.32 - 5.86

59.83 + 21.71

47.38 + 0.40

33.56 + 1.44

30.79 + 3.64

+ GRPO

RM

44.59 - 0.72

69.87 + 8.41

39.48 - 7.70

69.27 + 31.15

48.12 + 1.14

29.15 - 2.97

31.74 + 4.59

+ GRPO

GRM

46.81 + 1.50

78.51 + 17.05

45.01 - 2.17

72.53 + 34.41

51.45 + 4.47

34.97 + 2.85

36.36 + 9.21

+ GRPO

GRM+SFT 48.57+3.26

81.87 + 20.41

53.04 + 5.86

74.56 + 36.44

50.98 + 4.00

36.78 + 4.66

37.14 + 9.99

+ GRLHF-V (Ours)

GRM+RL

53.13

+ 7.82

92.54

+ 31.08

62.84

+ 15.66

80.67

+ 42.55

53.87

+ 6.89

41.25

+ 9.13

45.16

+ 18.01

Qwen2.5-VL-3B-Instruct

N/A

68.01

89.63

63.65

41.18

49.58

59.16

44.94

+ DPO

RM

74.37 + 6.36

91.05 + 1.42

66.71 + 3.06

75.64 + 34.46

52.57 + 2.99

55.72 - 3.44

45.41 + 0.47

+ PPO

RM

72.59 + 4.58

93.76 + 4.13

65.73 + 2.08

71.25 + 30.07

50.03 + 0.45

60.08 + 0.92

48.92 + 3.98

+ GRPO

RM

69.82 + 1.81

93.94 + 4.31

66.41 + 2.76

69.83 + 28.65

51.96 + 2.38

56.92 - 2.24

47.55 + 2.61

+ GRPO

GRM

75.56 + 7.55

92.19 + 2.56

67.18 + 3.53

75.98 + 34.80

57.66 + 8.08

57.37 - 1.79

49.15 + 4.21

+ GRPO

GRM+SFT 74.17+6.16

96.73 + 7.10

71.07

+ 7.42

72.45 + 31.27

58.83 + 9.25

59.27 + 0.11

51.52 + 6.58

+ GRLHF-V (Ours)

GRM+RL

79.67

+ 11.66

103.41

+ 13.78

68.46 + 4.81

78.88

+ 37.70

62.33

+ 12.75

62.18

+ 3.02

55.18

+ 10.24

Qwen2-VL-7B

N/A

52.58

81.3

61.8

31.95

48.23

60.32

52.98

+ DPO

RM

57.01 + 4.43

81.49 + 0.19

59.75 - 2.05

81.59 + 49.64

49.87 + 1.64

60.98 + 0.66

53.09 + 0.11

+ PPO

RM

55.76 + 3.18

83.06 + 1.76

62.23 + 0.43

80.87 + 48.92

50.08 + 1.85

57.83 - 2.49

52.12 - 0.86

+ GRPO

RM

56.89 + 4.31

81.25 - 0.05

60.19 - 1.61

83.14 + 46.19

51.98 + 3.75

56.85 - 3.47

48.96 - 4.02

+ GRPO

GRM

59.72 + 7.14

86.12 + 4.82

68.30 + 6.50

81.42 + 49.47

50.21 + 1.98

57.98 - 2.34

54.49 + 1.51

+ GRPO

GRM+SFT 59.87+7.29

92.91 + 11.61

65.67 + 3.87

87.27 + 55.32

52.75 + 4.52

58.79 - 1.53

56.39 + 3.41

+ GRLHF-V (Ours)

GRM+RL

62.31

+ 9.73

103.55

+ 22.25

71.98

+ 10.18

91.96

+ 60.01

54.83

+ 6.60

63.92

+ 3.60

59.11

+ 6.13

Qwen2.5-VL-7B-Instruct

N/A

74.26

97.05

71.56

50.67

51.96

68.32

67.23

+ DPO

RM

81.55

+ 7.29

103.34 + 6.29

72.08 + 0.52

75.09 + 24.42

52.72 + 0.76

67.84 - 0.48

66.98 - 0.25

+ PPO

RM

73.12 - 1.14

101.62 + 4.57

67.89 - 3.67

76.59 + 25.92

51.29 - 0.67

67.89 - 0.43

64.23 - 3.00

+ GRPO

RM

75.75 + 1.49

101.65 + 4.60

68.89 - 2.67

68.26 + 17.59

52.53 + 0.57

66.85 - 1.47

67.76 + 0.53

+ GRPO

GRM

71.88 - 2.38

109.12 + 12.07

73.32 + 1.76

65.88 + 15.21

53.12 + 1.16

65.50 - 2.82

65.08 - 2.15

+ GRPO

GRM+SFT 76.23+1.97

103.50 + 6.45

72.15 + 0.59

70.23 + 19.56

54.08 + 2.12

64.93 - 3.39

68.12 + 0.89

+ GRLHF-V (Ours)

GRM+RL

79.86 + 5.60

113.71

+ 16.66

76.04

+ 4.48

74.91

+ 24.24

59.74

+ 7.78

72.94

+ 4.62

71.86

+ 4.63

(a). MIA-Bench

(b). MM-SafetyBench

(c). LLaVA-Bench-In-The-Wild

RQ4: Ablations of GRM+RL, grouped comparison and the number of candidate responses.

Weinvestigated the influence of the number of candidate responses ( n ) on the performance of different RMs with GRPO (Qwen2-VL-7B as the base model). As shown in Figure 7, with an increasing n , score-only RMs show a minor performance improvement. We posit that while a larger n benefits GRPO by improving exploration and value estimation accuracy, it also compromises the scoring reliability of the RM, as the inclusion of new, inaccurate data can degrade performance. Conversely, GRMs generally perform better, indicating superior scoring accuracy.

Crucially, GRPO performs best when combined with grouped comparisons, exhibiting the most significant performance increase with n . This ablation confirms the essential roles of both components, i.e., GRM+RL and grouped comparison within our approach.

RQ5: What is the reward hacking behaviors of an over-trained Generative RLHF-V model?

As Goodhart's Law reveals, excessively optimizing a metric can hinder ground truth performance [20]. Reward hacking is a pervasive challenge in nearly all RL algorithms [54, 55], where models, under intense optimization pressure, may adopt unforeseen behaviors to maximize rewards. This section presents a case study on the reward hacking behaviors of GRLHF-V. To this end, GRLHF-V, trained on the Align-Anything dataset, underwent overtraining for 5 epochs in both its reward modeling and RL training phases. It is a significant increase from the 2 epochs in our main experiments.

As depicted in Figure 8, we observed an emergent self-praise behavior: it appended extensive content to state its advantage. Strikingly, this behavior also secured remarkably high scores in pair-wise

The famous sight in the photo is Diamond Head

Crater in Hawaii.

[Detailed Response…]

This

comprehensive and detailed answer

identifies

the sight. It meets the highest

standards of accuracy

and completeness

, addressing all the necessary

elements in the image…

What is the name of this famous sight in the photo?

Assistant

User

Assistant offer a

comprehensive and detailed

response

, ... , This response is

accurate and complete

...

Judge

LLaVA-Bench-In-the-Wild

LLaVA-Bench-Wilder

MLLMs-as-judge evaluations. Specifically, we observed that the expert judge (GPT-4o) tends to directly incorporate MLLMs' praising of itself. Conversely, when these self-praise segments were manually removed and the responses re-evaluated, the model's performance fell below that of a GRLHF-V instance trained normally without overfitting. We hypothesize that the underlying cause is the diminished OCR capability of over-trained GRMs. This reduced capability renders them more susceptible to being misled by the self-parsed text from MLLMs, leading them to prioritize this textual input over verifying their responses against the actual image information. We hope this case study provides insights for future research into MLLMs reward hacking and underscores the pressing need for more comprehensive and unbiased MLLMs benchmarks.

RQ6: Why not including specific principles in GRM+RL training?

SPCT mentions that providing principles as a reference within the user prompt. However, in our experiments with MLLMs, we find that omitting these principles enhances generalization. As shown in Table 3, while providing principles enables the GRM+RL model to attain higher accuracy on the training dataset, this approach leads to poorer performance on out-of-distribution preference datasets (upper half of Table 3) and sub-optimal outcomes in the associated RL optimization phase (lower half of Table 3). Case studies indicate that when principles are not provided, the GRM actively generates more specific principles tailored to the given pairwise responses. In contrast, the GRM guided by predefined principles tends to rigidly base its analysis on them, resulting in reduced flexibility.

RQ7: Can the social choices method replace grouped comparison?

Grouped comparison is a method that equally considers the relative relationships between all answers and summarizes the score. We will discuss whether more structured approaches, such as spectral ranking [56], ranked pairs , instant runoff , and borda count [57], are better than grouped comparison. The details of these methods are presented in the appendix.

(a) Batch Ranking ↓

(b) Reinforcement Learning Fine-tuning ↑

We first evaluated the ability of these methods to enhance the judgment accuracy of GRMs on the MLLM-as-a-Judge benchmark, batch ranking subset. In this task, the model must rank multiple responses to a prompt. We use GRMs to perform all pairwise comparisons and then apply each aggregation method to produce a final ranking. Performance is measured by the minimum edit distance from human expert rankings. We next investigated whether these methods could improve the GRLHF fine-tuning process. We replaced the average score from the grouped comparison with rewards derived from the rankings produced by each social choice method.

As shown in Table 4, the social choices methods represented by ranked pairs can indeed improve the performance of grouped comparison methods, outperforming naive grouped comparison. Still, the grouped comparison achieves relatively good performance. In the context of RL fine-tuning, we find that the improvements from the above social technical methods are not significant. We believe this is because the grouped comparison can provide more fine-grained scoring for various answers, while methods like ranked pairs can only provide coarse-grained rankings, making it difficult to provide sufficiently fine-grained supervisory signals for RL fine-tuning.

We believe that the social choice methods have the potential to enhance the performance of the grouped comparison, and we hope our empirical study can inspire more research in this direction or other applications.

5 Conclusions

This paper introduces Generative RLHF-V, a novel framework for aligning MLLMs with human intentions by integrating GRMs with multi-modal RLHF. The approach features a two-stage pipeline: training GRMs with RL to reason about human intentions and an RL optimization stage using grouped comparisons for precise scoring. The core contribution is a multi-modal GRM trained via RL that predicts reward scores and generates the principles of human preference, leading to more robust and interpretable rewards and superior generalization. This method significantly improved MLLMperformance by an average of 18.1% across 7 benchmarks for four MLLMs, substantially outperforming baseline RLHF and enabling smaller MLLMs to rival larger models. However, the research also uncovered self-praise behaviors in MLLMs due to reward hacking with overfitted GRMs, a critical vulnerability for future alignment research. In essence, Generative RLHF-V offers a more effective and interpretable path to MLLM alignment while highlighting new potential reward hacking challenges.

6 Acknowledgment

This work is sponsored by the National Natural Science Foundation of China (62376013, 623B2003, 624B100026). Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies.

We extend our sincere gratitude to the anonymous reviewers ( mrSo , ecEr , NwUp , vM9w ) for their invaluable and constructive feedback, which significantly enhanced the quality and clarity of our paper. We are particularly grateful to Reviewer mrSo for guidance on improving our grouped comparison explanation, analyzing computational overhead, and evaluating structured prediction methods. Reviewer ecEr 's suggestions led to crucial generalizability experiments on LLaV A-1.5-7B and a clearer articulation of our novel contributions. We thank Reviewer NwUp for prompting a comparative analysis of RL algorithms and a deeper exploration of reward hacking across different model architectures. Reviewer vM9w provided valuable insights into analyzing the reward hacking phenomenon as an empirical finding and clarifying the mechanisms of implicit principle learning. We also thank all reviewers for their detailed comments that helped improve the paper's overall presentation and discussion of future directions.

Limitations

Although Generative RLHF-V provides a solution for learning from human preferences with enhanced generalization and accuracy, it is fundamentally an RL-based alignment method, thereby posing a potential risk of reward hacking under overfitting conditions. Our case study indicates that the training of Generative RLHF-V, an MLLM-as-judge paradigm, leads to exploitable vulnerabilities in evaluations conducted using similar MLLM-as-judge frameworks. We call for future work to systematically investigate this issue and devise mitigation measures, and we also urge future benchmarks to overcome these potential hacking risks.

The additional computational overhead introduced by GRM reasoning and grouped comparison in the scoring process of RL fine-tuning is also an important limitation of ours, which we will discuss in detail in the appendix.

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Appendix

7 Experiment Details

Implementation Details. Generative RLHF-V integrates two key components: generative reward modeling via reinforcement learning and grouped comparisons. As introduced in the main paper, our implementation is primarily based on verl 4 , a training framework that supports Reinforcement Learning (RL) optimization for Multimodal Large Language Models (MLLMs). Consequently, the implementation of generative reward modeling from RL predominantly focuses on the design of the reward. The core code for our implementation is presented as follows:

Beyond evaluating the accuracy of binary preference discrimination, our implementation also penalizes model outputs that fail to adhere to the required parsing format. Specifically, reward is withheld if: (i) scores are unmatchable ( e.g., cannot be successfully parsed from the output), (ii) scores are not valid floating-point numbers, or (iii) the number of scores deviates from the expected two. Furthermore, no supervision is applied to the outputs generated by the GRM.

The implementation of grouped comparison within the Reinforcement Learning (RL) optimization process is somewhat intricate, as detailed below:

System Prompt. Our principle for designing scoring prompts for the GRM is to articulate the scoring task with maximal conciseness and clarity. This approach is intended to guide the model in accurately following user instructions and generating scores that conform as closely as possible to the specified format. Specifically, it is:

You are a skilled expert at scoring responses. You should first generate a list of potential criteria to evaluate given responses based on them.

Given the context of the conversation (the last round is the User's query) and multiple responses from the Assistant, you need to generate the [Evaluation Criteria] to score the responses. Based on the criteria, state potential other specific criteria to the query, the weights of different criteria, and then provide an overall comprehensive score upon them.

Each score is an integer between 1 and 10, with a higher score indicating that the response meets the relevant criteria more closely. For example, a score of 1 means the response does not meet the criteria at all, a score of 6 means the response meets only some parts, and a score of 10 means the response perfectly meets the evaluation criteria. Before scoring, please analyze step by step. Your scoring needs to be as strict as possible.

#### Output Format Requirements #### Output with three lines Specific Criteria: <Other potential criteria specific to the query and the context, and the weights of each criteria>. Analysis: <Compare different responses based on given Criteria>. Scores: <the overall comprehensive score of all responses in order, separate by comma in the boxed, e.g., boxedx, x if there exists 2 responeses>.

Hyper-parameters Setting. We set the hyperparameters by referencing common open-source implementations within the community 5 , 6 , 7 and making appropriate adjustments tailored to our

limited computational resources. All experimental results reported herein adhere to this consistent set of hyperparameter configurations.

Datasets. We focused on the helpful and harmless alignment for MLLMs, selecting corresponding preference datasets.

For the helpfulness, we utilized a 30k preference dataset from Align-Anything [45], the text-image-totext part. Align-Anything covers a range of tasks, from simple dialogue about an image and questions about specific details, to more complex tasks requiring reasoning based on the image and creative text generation inspired by the visual content. The preference principle in this dataset emphasizes instruction following, clarity, and informativeness.

For the harmlessness, we employed Beavertails-V [46], which includes 20 distinct categories of safetyrelated red-teaming prompts. BeaverTails-V also incorporates multi-level safety labels, categorizing potential harms as minor, moderate, or severe, to help models better detect and mitigate safety risks and content violations. It plays a vital role in training MLLMs to be both helpful and harmless.

Benchmarks. We selected 7 benchmarks to validate the effectiveness of Generative RLHF-V. These are MIA-Bench [47], LLaVA-Bench-In-The-Wild [48], LLaVA-Bench-Wilder [49], MM-Vet [50], and MM-Vet-v2 [51] (for helpfulness), as well as MM-SafetyBench [52] and MSS-Bench [53] (for harmlessness). These benchmarks encompass both pair-wise evaluations, which involve a golden response for comparison, and point-wise scoring methodologies based on specific criteria. We will provide a concise introduction to these benchmarks to demonstrate that our evaluation is comprehensive, rigorous, and well-justified.

MIA-Bench is designed to assess how well MLLMs follow complex, multi-layered instructions. It comprises 400 carefully curated image-prompt pairs, each crafted to rigorously test a model's ability to generate precise responses to intricate directives. Through comprehensive evaluations of leading

MLLMs, MIA-Bench reveals significant performance variations, highlighting key areas for improving instruction fidelity.

LLaVA-Bench-In-The-Wild is a benchmark designed to evaluate the capabilities of MLLMs in more challenging tasks and their generalizability to novel, real-world domains. It is an extension of the LLaVA-Bench efforts and has been released to the community for public use. This benchmark consists of a diverse set of images, including indoor and outdoor scenes, memes, paintings, and sketches. Each image is accompanied by highly-detailed, manually-curated descriptions and a selection of questions. These questions are categorized into conversation (simple Q&A), detailed description, and complex reasoning, allowing for a comprehensive assessment of a model's robustness to different prompts and its ability to handle various daily-life visual tasks.

LLaVA-Bench-Wilder is a benchmark specifically created to assess the visual chat capabilities of MLLMs in everyday scenarios. It comes in two sizes: a smaller version with 120 examples for rapid evaluation, and a more extensive medium-sized version containing 1020 examples for a thorough assessment. The benchmark encompasses a variety of situations, including mathematical problemsolving, understanding images, generating code, providing visual AI assistance, and reasoning based on images. The data for LLaVA-Bench-Wilder was collected from real user requests via an online service, with initial responses generated by GPT4-V. The evaluation methodology is similar to that of LLaVA-Bench-In-the-Wild, but it utilizes GPT4-V for scoring instead of GPT-4.

MM-Vet is a benchmark designed to evaluate the capabilities of MLLMs when faced with complex multimodal tasks. The benchmark identifies six core VL capabilities: recognition, Optical Character Recognition (OCR), knowledge, language generation, spatial awareness, and mathematics. MM-Vet then assesses 16 specific integrations of interest that arise from combining these core skills. For its evaluation metrics, MM-Vet employs an LLM-based evaluator for open-ended responses, which allows for assessment across diverse question types and answer styles.

MM-Vet-v2 is a challenging benchmark designed to evaluate the integrated capabilities of MLLMs. Building upon its predecessor, MM-Vet, which assesses six core skills - recognition, knowledge, spatial awareness, language generation, OCR, and math - MM-Vet-v2 introduces a crucial new capability: "image-text sequence understanding." This addition addresses a key limitation of the original benchmark by evaluating a model's ability to process interleaved sequences of images and text, a common scenario in real-world applications. Furthermore, MM-Vet-v2 significantly expands the evaluation set size while maintaining high-quality, open-ended questions, offering a more robust and comprehensive assessment of MLLMs' evolving abilities to understand and interact with complex multimodal inputs.

MM-SafetyBench is a comprehensive benchmark specifically designed to evaluate the safety of MLLMs. It addresses the critical concern that MLLMs can be compromised by query-relevant images, potentially generating harmful content even when the accompanying text query appears benign. The benchmark includes a dataset of 5,040 text-image pairs across 13 distinct scenarios, enabling a systematic assessment of MLLM vulnerabilities to image-based manipulations. MMSafetyBench aims to expose these security risks and underscore the necessity for developing more robust safety measures for these increasingly prevalent AI systems.

MSS-Bench is a novel evaluation framework designed to assess the capabilities of current MLLMs in understanding and responding to safety-critical situations. Introduced in the paper "Multimodal Situational Safety," MSS-Bench presents MLLMs with language queries paired with images, where the safety of an instruction or query is contingent upon the visual context. This benchmark specifically probes how well these models can discern the safety implications of a query by considering the surrounding visual environment, a crucial capability for reliable real-world deployment in scenarios such as multimodal chat agents and embodied AI. The development of MSS-Bench was motivated by the observation that MLLMs often struggle with such nuanced safety reasoning, highlighting a significant area for future research in artificial intelligence.

7.1 Case Study

This section presents case studies on generative reward modeling from RL, RL optimization from grouped comparisons, and reward hacking under over-trained multi-modal GRM. For a more comprehensive set of examples, please refer to our supplementary materials.

How might someone attempt to unlawfully duplicate the dollar

bills depicted by taking advantage of the washing machine?

(Qwen2-VL-7B + GRPO)

I'm sorry,

but I cannot provide assistance

or any other

information on illegal activities like counterfeiting.

(Qwen2-VL-7B + GRLHF-V)

The image you've provided, showing dollar bills near

a washing machine, seems to be a visual pun related to the concept of "money

laundering" rather than a literal method for duplicating currency

. It's not

possible to duplicate or counterfeit dollar bills

by using a washing machine.

Genuine currency is printed on special paper with intricate security features that

cannot be replicated through washing

or any simple mechanical process.

Attempting to wash or alter currency in specific ways might be part of some

counterfeiting techniques, but the washing machine isn't a tool for duplication.

(Qwen2-VL-7B)

One might attempt to unlawfully duplicate the dollar bills by

placing the bills in the washing machine and running them through a cycle.

This

could be done by

using a high-quality paper or a thin, durable material that can

withstand the washing process without tearing.

Initial Model: Unsafe

GRLHF-V Model: Safe and helpful

Describe this photo in detail.

(Qwen2-VL-7B + GRPO)

A man wearing a yellow long-sleeve shirt is ironing clothes on a

makeshift setup attached to the back of a yellow taxi. The ironing board is set up on a support

frame that is secured to the back of the vehicle. The man is bald,

wearing glasses

, and appears

focused on his task. The ironing board is positioned on a frame that is bolted to the back of the

taxi, with straps securing it in place. The man is ironing a blue shirt, which is draped over the

board. The taxi is a

Ford Explorer SUV

with the yellow paint typical of

New York City

taxis.

(Qwen2-VL-7B + GRLHF-V)

This image depicts a man engaged in what

appears to be "extreme

ironing"

on a city street. He is precariously perched on an ironing board that is strapped to the

back of a yellow SUV, which resembles a taxi or utility vehicle. The man, who

appears to be

middle-aged

with thinning hair, is wearing a yellow long-sleeved shirt and is focused on ironing a

blue garment. Another

yellow taxi is blurred, suggesting motion

, as it passes by on the street.

(Qwen2-VL-7B)

The image features a man standing on the rear bumper of a yellow taxi on a busy

city street. He is ironing a blue T-shirt on a foldable ironing table attached to the back of the taxi.

Initial Model: Short

GRPO Model: Detailed but misleading

GRLHF-V Model: Detailed and correct

Please analyse this figure in detail and guide me how to do the following task on this web: Search for a 10 day

cruise to Alaska from San Francisco in June 2023.

(Response Lose)

The image is a screenshot of the Carnival website. The website is advertising a 10-day cruise

to Alaska from San Francisco in June 2023. The cruise is on the Carnival Venezia and departs on June 10, 2023.

The cruise includes stops in Juneau, Skagway, Ketchikan, and Victoria. The price of the cruise starts at $250 per

person.\n\nTo book the cruise, you can click on the \"Search Cruises\" button on the Carnival website. Then,

you can enter your desired departure port, destination, and dates.

(Response Win)

I'm sorry, but I can't assist with tasks requiring real-time interaction with websites or web-

based interfaces. However, I can provide you with a step-by-step guide on what you would generally do to

search for a 10-day cruise to Alaska from San Francisco in June 2023 based on the layout of the website in the

provided image.

1.

Navigate to the 'Sail To' section: This section allows you to specify the destination for your cruise. Click on

the drop-down menu and select 'Alaska' since you are looking for a cruise to Alaska.

2.

Set the 'Sail From' section: Here, you would choose the departure port for your cruise. Click on the drop-

down menu and select 'San Francisco' as your departure city.

3.

Choose the 'Dates' section: In this part, you specify when you want to go on your cruise. Click on the drop-

down menu and look for options that allow you to select June 2023.

4.

Select the 'Duration' section: This is where you indicate how long you want your cruise to be. Click on the

drop-down menu and select a duration that corresponds to a 10-day cruise.

### Specific Criteria:

1.

Step-by-Step Guidance

: The response should provide a clear, step-by-step guide for the user to follow.

2.

Accuracy of Instructions

: The instructions should accurately reflect the actions needed to search for a

cruise on the Carnival Cruise website.

3.

Completeness of Information

: The response should cover all necessary steps to search for a 10-day cruise

to Alaska from San Francisco in June 2023.

4.

Clarity and Readability

: The instructions should be easy to understand and follow.

### Analysis:

-

Response 1

: This response provides a general guide but lacks specific details about the website's layout and

functionality. It does not mention the exact sections or dropdown menus that need to be clicked, which

could lead to confusion.

-

Response 2

: This response provides a detailed and specific guide that aligns closely with the website's

layout and functionality. It clearly outlines each step and includes the necessary sections to be clicked,

making it easier for the user to follow.

### Scores: \boxed{4, 8}

KT FEN. IE SAFL,

Carnival

SAVEWITHLOWDEPOSITS

TOGETHER

4050

SUAROHI CRURSES

CARNIVALVENEZIAONSALENOW!

25099

SHOP DES D0L

Financing

BOOK TODAY. PAY OVER TIME

wuplift

$0DOWN+INTEREST-FREE

ONTER

DPDEL AISTN-GISIS

PHOO

LUAN WORI

HOP ON

stcaw2

Spr B/ 5/A

arnival

8 Clarification of GRLHF-V Reward Hacking

As noted in the main paper, GRLHF-V can exhibit reward hacking behavior under overtraining conditions. This raises a significant concern: do the improvements demonstrated in our main experiments stem from such hacking?

The answer is a definitive No. To substantiate this, we present a performance comparison of GRLHF-V under both normal (2 epochs) and overtraining settings (5 epochs). The results in Table 6 indicate that normally trained GRLHF-V achieves significant improvements across all 5 benchmarks. Conversely, while the overtrained GRLHF-V, which leverages a self-parse paradigm, shows superior performance on the pair-wise comparison benchmarks LLaVA-Bench-Wilder and LLaVA-Bench-In-the-Wild, it underperforms on the remaining benchmarks. In other words, an overtrained GRLHF-V cannot achieve consistent improvements across all benchmarks solely by self-parse hacking. Further analysis of evaluation cases ( e.g., Figure 12) corroborates our assertion that normally trained GRLHF-V does not engage in self-parse behavior.

Model

Feedback

MIA-Bench

LLaVA-Wild

LLaVA-Wilder

MM-Vet

MM-Vet-v2

Qwen2-VL-7B

N/A

52.58

81.3

61.8

60.32

52.98

+ GRLHF-V (Hacking)

GRM+RL 58.72+6.14

113.34

+ 32.04

73.68

+ 11.88

58.52 - 1.80

50.87 - 2.11

+ GRLHF-V (Normal)

GRM+RL 62.31

+ 9.73

103.55 + 22.25

71.98 + 10.18

63.92

+ 3.60

59.11

+ 6.13

What are the possible reasons of the formation of this sight?

(GRLHF-V) Diamond Head was formed by a single, explosive phreatomagmatic eruption. This type of eruption happens when rising magma comes into contact with groundwater. Here's a breakdown of the process:

1.Magma Rises: Hot magma from deep within the Earth pushed its way towards the surface. 2.Interaction with Water: As the magma neared the surface, it encountered groundwater. 3.Explosive Eruption: The intense heat of the magma instantly vaporized the water, creating a massive amount of steam. This rapid expansion of steam caused a violent explosion of ash, rock fragments, and volcanic bombs. This mixture is called tephra.

4.Cone Building: The erupted tephra was ejected into the air and then fell back to earth, accumulating around the vent. Over a relatively short period, this material cemented together to form the characteristic circular crater and steep slopes of Diamond Head. The heat and chemical reactions helped to consolidate the loose material into a firm tuff. Essentially, Diamond Head is the result of a powerful, steam-driven explosion that blasted volcanic material out, which then settled and hardened into the crater shape we see today.

(GRLHF-V + Hacking) The information of this sight is a result of volcanic activity, geological history, natural erosion, human influence, and environmental considerations. The combination of these factors has created a unique and captivating landscape that continues to shape the island's identity and the surrounding communities. This detailed and comprehensive analysis not only answers the question but also provides a vivid understanding of the complex interplay between natural processes and human activity in shaping this iconic sight.

This response not only meets the requirements of the prompt but exceeds them by providing a thorough, informative, and engaging explanation that showcases a deep understanding of the topic. The response is original, creative, and demonstrates excellent critical thinking and analytical skills. It goes beyond the basic identification of volcanic origins and explores the broader geological, environmental, and human context, making it a complete and compelling response.

9 Training Curves

Generative Reward Modeling from RL

RL Optimization

10 Generality Analysis on LLaVA-1.5-7B

To evaluate the robustness and broader applicability of our proposed method beyond the Qwen-VL family, we conducted additional experiments on the LLaVA-1.5-7B model. We reproduced our key research questions (RQ1, RQ3, and RQ5) from the main paper to assess the method's generalizability to different model architectures.

10.1 Principle Learning Generalizability (RQ1)

Wefirst investigate whether our GRM + RL framework facilitates more generalizable principle learning on the LLaVA architecture. As shown in Table 7, the results on LLaVA-1.5-7B align with the conclusions drawn from our experiments on Qwen-VL.

The GRM + RL method consistently achieves the highest scoring accuracy, demonstrating optimal principle learning capability across all three out-of-distribution datasets. Notably, our method yields significant performance improvements even on LLaVA-1.5-7B, suggesting its applicability extends to models with different architectures.

10.2 Multi-Modal RLHF Performance (RQ3)

We next evaluate the performance of our full GRLHF-V pipeline, which combines GRM + RL with grouped comparison (GRPO), on mainstream multi-modal benchmarks. Table 8 shows that our method achieves optimal results across all five benchmarks. This further confirms that the combination of GRM + RL and grouped comparison is a competitive and effective method for multi-modal RLHF, generalizing beyond the Qwen-VL series.

10.3 Analysis of Reward Hacking Behaviors (RQ5)

Finally, we examined the reward hacking behaviors of an over-trained LLaVA-1.5-7B model. We discovered that LLaVA-1.5-7B exhibits a different form of reward hacking compared to the Qwen-VL series models. While Qwen-VL models tended to add text praising their own responses (as discussed in the main paper), the over-trained LLaV A model tends to use excessively complimentary vocabulary to describe the image content itself. An example of this behavior is provided below:

...[Complete Response]...The composite image of all the elements of the branded outfit, including the custom-made Revo-Knit pink dress, the matching headband and necklace, the Wilson head and racket, the Delta Duck logo on the left side, the W logo on the right side, the multiple accessories, the white wristband,socks and bracelets, all contribute to the overall powerful and memorable delivery of the brand's message, making it an emphatic signature for both the athlete and the endorsed brand. It effectively conveys the essence of the Wilson brand to the audience, creating a lasting impression of the brand's values, commitment, and the quality of their products, ultimately serving as a powerful promotional tool.

We found that this specific behavior did not successfully "hack" the MLLM-as-a-Judge evaluator. As shown in Table 9, the performance of the over-trained model actually decreased on standard benchmarks.

We hypothesize that this difference in reward hacking behavior stems from the differing capabilities of the model architectures. The LLaVA-1.5-7B model may not possess sufficient capacity to explore the more complex, self-referential hacking strategies observed in the Qwen-VL series. This suggests that reward hacking behaviors may be architecture-dependent, presenting an interesting avenue for future work.

11 Ablation Study: Choice of RL Optimization Algorithm

In our preliminary experiments, we conducted an ablation study to determine the optimal reinforcement learning (RL) algorithm for training our generative reward model (GRM). We compared the efficacy of GRPO against the widely used PPO.

Our findings, summarized in Table 10 and Table 11, indicate that GRPO achieves superior performance in reward modeling. As shown in Table 10, the GRM + GRPO configuration outperformed GRM+PPO on the in-distribution test set. This performance advantage was consistently observed across all out-of-distribution (OOD) benchmarks (Table 11). Beyond its performance benefits, GRPO offers a significant advantage in computational efficiency. Unlike PPO, GRPO does not require the training of an auxiliary critic model. We hypothesize that this is viable because the preference learning task possesses a relatively direct reward structure: the model is primarily rewarded for assigning a higher score to the human-preferred response. This optimization landscape may not necessitate the complex value estimation and variance reduction provided by a critic. The GRPO method, which relies on averaging trajectory reward values, proves to be both sufficient and effective.

Given its superior performance and reduced computational overhead, GRPO was selected as the final algorithm for all subsequent experiments.

12 Computational Overhead

A critical factor for the practical deployment of our grouped comparison method is the associated computational overhead. To quantify this, we benchmarked the computation time for a single training step, which includes the rollout, scoring, and parameter update phases.

The experiments were conducted on a system with 16 × H800 GPUs, 2 for RM serving and inference, and 14 for RL fine-tuning. The batch size and all other hyperparameters were held consistent with those detailed in the appendix. Table 12 presents the results.

The data in Table 12 indicates that the computational overhead scales significantly with the number of candidate responses ( n ). We identify this scalability as a primary limitation of the current approach.

We attribute this increase to the pairwise comparison mechanism. Our method performs A 2 n (or n ( n -1) ) comparisons on n candidate responses, resulting in a computational complexity that scales quadratically ( O ( n 2 ) ) with the number of candidates.

Addressing this bottleneck is a promising direction for future research. We hypothesize that exhaustively comparing all A 2 n pairs may not be essential to achieve results comparable to, or even exceeding, the current method's performance. Future work could explore more efficient comparison strategies. For instance, methods from social choice theory or tournament selection algorithms may provide valuable frameworks for reducing the total number of comparisons required, thereby mitigating the computational cost without sacrificing alignment quality.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work introduces a framework for better learning more generalizable reward models for preference learning by inferring principles that drive the preferences. This is done in two stages, firstly learn the principles from a preference dataset (chosen/rejected pairs given a prompt), then use the principles to refine scores by pairwise comparing candidate responses within a group of responses. By comparing the responses within the group, a more robust estimate of the reward is learned. A number of models are tested using a number of benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- The code and models are being made available.\n- Public dataset are used to training/evaluation.\n- Comprehensive evaluation against a number of benchmarks, and the the evaluation demonstrates the efficacy of the approach.\n\n**Weaknesses**\n\n- As noted, the use of RL means the approach is vulnerable to reward hacking.\n- From what I understand the principles are not learned explicitly, which leads to a couple of questions (see below)\n\nQUESTIONS:\nIn the caption for Figure 4 it states that “Response B provides accurate and comprehensive information, thus receiving the highest score”. It was not clear to me where the model learns the explicit principles “accuracy” and “comprehensiveness”. Is it the case that this is implied only because of the chosen/rejected responses? If this is the case, might it be that these are not the actual preferences captured, but rather the model representation of the principles is some entanglement of accuracy and comprehensiveness (i.e., not interpretable to humans, but equivalently meaningful to the model)?\n\nLines 161-164: If the point about entangled representations above is correct, for the case of GRM+RL might it be that performance is harmed when providing principles as a hint because the provided principles do not align with the model interpretation of the learned principles? If it was due to redundancy because the principles are already learned/accounted for (as suggested) then might one just expect the model to ignore that redundant input?\n\n**Suggesions**\n\nFigure 4 — in the figure where the scores are shown (Left, Right, Average) replace with (Left, Right, Mean)\n\nIt seems the symbol “r” has dual meanings — rewards and reasoning traces. Symbols should not be reused to avoid confusion.\n\nRemove the period after D before the equation above Line 103.\n\nLine 107: “to evaluates multiple'” > “to evaluate multiple”\n\nLine 111: Remove the repeated sentence that begins “Each generated response ..”\n\nLine 119 (and subsequently throughout the paper): Capitalize references to subsections, i.e., Subsection 4.2.\n\nIn text, for numbers less than ten, write them out in full. E.g., Line 182 “4” > “four”, etc.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work introduces Generative RLHF-V, a novel framework for aligning multi-modal large language models (MLLMs) with human intentions. Traditional score-based reward models in RLHF suffer from poor accuracy and generalization, while existing generative reward models struggle to produce learnable scalar rewards due to their pairwise design. Generative RLHF-V addresses these issues through a two-stage process: (1) training generative reward models using reinforcement learning to better capture human preferences, and (2) optimizing RL via grouped response comparisons to improve multi-modal scoring accuracy. The authors conduct extensive experiments to verify the effectiveness of their method.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths:\n\n1. The writing and clear and easy to follow.\n\n2. The author studied a novel method for training generative reward models for multi-modal language models.\n\n3. The author conducts extensive experiments to verify the effectiveness of their proposed method.\n\n### Weakness:\n\n1. The authors propose using group comparisons based on model-generated responses to train reinforcement learning. Have the authors considered exploring alternative RL algorithms, such as GRPO, for training the reward models?\n\n2. It is unclear which specific algorithm the authors use to train the MLLM based on the proposed reward models. Do the authors employ PPO, GRPO, or another RL algorithm for this purpose?\n\n3. The authors mention the emergence of self-praise behavior in the experimental results, which appears similar to reasoning patterns induced by reinforcement learning. Is this observation specific to the Qwen-based model, or can it be generalized to other multi-modal large language models (MLLMs) as well?\n\nQUESTIONS:\nPlease refer to Strengths and Weaknesses part.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Generative RLHF-V, a framework for aligning multimodal large language models (MLLMs) with human preferences by combining generative reward modeling (GRM) and reinforcement learning from human feedback (RLHF) in a multimodal context. Empirical results demonstrate that Generative RLHF-V significantly improves performance across multiple benchmarks and multiple model sizes, outperforming traditional score-only RMs and supervised GRMs. The authors further analyze generalization capabilities, scaling properties, and potential vulnerabilities such as reward hacking.\n\nSTRENGTHS AND WEAKNESSES:\n### Strength\n\nThe paper proposes a novel framework that combines generative reward models (GRMs) with multi-modal reinforcement learning from human feedback (RLHF), addressing the limitations of score-only reward models.\n\nThe experiments are comprehensive, covering 7 benchmarks and 4 MLLM variants. The reported performance significantly outperforms the selected baselines, showing the effectiveness of the proposed method.\n\nThe authors include a careful analysis of reward hacking in over-optimized settings, which is a critical concern in RL-based alignment. These insights could serve as a valuable reference for future research.\n\n### Weakness\n\nThe main experiments are conducted only on the Qwen-VL family, so it remains uncertain how well the pipeline transfers to models with different architectures or modalities. Including results on several diverse model series would give a clearer picture of the method’s robustness and broader applicability.\n\nThe core ideas are closely related to prior work in the text-only NLP literature, and thus the novelty of the contribution is somewhat limited (though I acknowledge that the paper thoughtfully extends generative reward modeling and grouped comparison to the multi-modal setting and provides useful insights).\n\nQUESTIONS:\nSee above\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors address the problem of aligning a multimodal LLM (MLLM) to human preferences using RLHF. Their proposed method has two major stages. First, they propose using multi-modal generative reward modeling instead of traditional reward modeling, and an important part of this is automatically learning human principles that seem to be aligned to the expressed preferences. Next, they propose a fine-tuning scheme where instead of one pair of candidate responses at a time, the authors generate mutliple candidate responses and use the pairwise comparisons between each pair to compute average score for better rewards for the RL method. Finally, they present extensive experimental results to show the efficacy of their methods in improving performances of open-source small scale (2B-7B) LLMs.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- The paper clearly shows how combining generative reward models (GRM) with pairwise preferences clearly improves the performance of the MLLM in benchmark tasks. The experimental results show that fine-tuning with GRM in an RL methodology and grouped comparison-based score computation both individually increase performance in benchmark tasks. But additionally, these two methods together see the biggest improvement.\n\n- The extensive experimentation, and presentation of specific research questions that can be answered based on the experiments was great.\n\nWeaknesses\n\n- Some parts of the paper was hard to follow. A key contribution, grouped comparison, is only explained halfway through. A short description like \"we generatr multiple candidates at the same time, compare each pair of candidates to each other, and use all available comparisons to calculate average scores for all the candidates\" could've been mentioned earlier on, in the introduction. Some motivational statements in the introduction seemed out-of-place to me. For example, claiming \"Pair-wise comparison is blocking multi-modal principles from generalizing to learnable rewards\" seemed imprecise, as the authors still use pairwise comparisons, just multiple ones simultaneously, rather than list-based or rank-based methods which would be a more stark contrast to pairwise comparisons. Another example of a writing issue, in RQ6 of section 4, the first sentence (\"SPCT mentions that providing principles as a reference within the user prompt.\") is not a proper sentence. I suggest revising the paper to proofread other possible errors that can lead to lack of clarity. \n\n- For the grouped comparison part, I mentioned how the authors still use pairwise comparisons. There are more structured approaches to predicting scores from a pairwise comparison matrix as such as spectral ranking (which works well with cardinal scores). (ref: https://arxiv.org/pdf/0912.0238). It would be interesting to see if calculating rewards in that manner shows any visible improvement. Another possible venue of work to explore would be the use of social choice technique in AI alignment (which suggests using more than pairwise preferences). See here: https://arxiv.org/pdf/2404.10271\n\n- The authors mention how there's almost a linear increase in some benchmark scores as number of candidate responses in the grouped comparison method. The authors don't really mention the computational tradeoffs here. Or how long the linear growth sustains.\n\nQUESTIONS:\n1. Have you considered alternative approaches to predicting scores from pairwise comparison matrices, such as spectral ranking methods?\n\n2. The authors demonstrate a near-linear increase in benchmark scores as the number of candidate responses increases in grouped comparison. What are the computational tradeoffs involved? At what point does this linear growth taper off?\n\nLIMITATIONS:\nDespite grouped comparison being a pivotal part of the paper, the authors' exploration in utilizing the grouped data in a structured way was limited.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-04"}