ExPO-HM: Learning to Explain-then-Detect for Hateful Meme Detection
Authors: Jingbiao Mei
Mingsheng Sun
Jinghong Chen
Pengda Qin
Yuhong Li
Da Chen
Bill Byrne
Venue: ICLR 2026 Poster (2026)
Keywords: Hateful Meme Detection
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Hateful memes have emerged as a particularly challenging form of online abuse, motivating the development of automated detection systems. Most prior approaches rely on direct detection, producing only binary predictions. Such models fail to provide the context and explanations that real-world moderation requires. Recent Explain-then-Detect approaches, using Chain-of-Thought prompting or LMM agents, perform worse than simple SFT baselines, and even advanced post-training methods such as GRPO fail to close the gap. Our analysis identifies two key issues of such systems: important policy-relevant cues such as targets and attack types are not hypothesized by the model as a likely explanation; and the binary reward signal is insufficient to guide reasoning. To address these challenges, we propose ExPO-HM (Explain-then-Detect Policy Optimization for Hateful Memes), inspired by the training and evaluation process of human annotators. ExPO-HM combines SFT warmup, GRPO with curriculum learning, and Conditional Decision Entropy (CDE) as both metric and reward for reasoning quality. Across three hateful meme benchmarks, ExPO-HM achieves state-of-the-art performance on binary detection, fine-grained classification, and reasoning quality, with up to 15\% and 17\% F1 improvement over the GRPO and DPO baselines, respectively. By moving hateful meme detection from simple binary alarms to explanation-driven detection, ExPO-HM provides accurate, interpretable, and actionable moderation support. Code available at: https://github.com/JingbiaoMei/ExPO-HM

Published as a conference paper at ICLR 2026
EXPO-HM: LEARNING TO EXPLAIN-THEN-DETES FOR HATEFUL MEME DETECTION
Jingbiao M Mingsheng Sun Jinghong Chen', Pengda Qin", Yuhong Li, Da Chen'; Bill Byrne :
'Department of Engineering, University of Cambridge, "Xiaohongshu Inc., UUniversity of Bath, *enneet Company, China, AAlibaba Group jm224 mmn2450cam.co ac. uk, sunms55 3 1. com, jc2 12 48cmm. ac. uk, derekpdqi www.ineeeeeettommmmmmmmmmmm com, daniel. yuhong@gmaii 1 com, da. chen@bath. edu, wjb310camm ac.uk
ABSTRACT
Hateful memes have emerged as particularly challenging form of online abuse, motivating the development of automated detection systems. Most prior ap- proaches rely direct detection, producing only binary predictions. Such mod- els fail to provide the context and explanations that real-world moderation re- quires. Recent Explain-then-Detee approaches, using Chain-of-Thhughhe prompt- ing or LMM agents, perform worse than simple SFT baselines, and even ad- vanced post-training methods such GRPO fail close the gap. Our analysis identifies two key issues of such systems: important policy-relevant cues such as targets and attack types are not hypothesized by the model likely expla- nation; and the binary reward signal is insufficient to guide reasoning. To ad- Policy Op-
dress these challenges, we propose EXPO-HM (Explain-then-Deiee timization for Hateful Memes), inspired by the training and evaluation process human annotators. ExPO-HM combines SFT warmup, GRPO with curricu- lum learning, and Conditional Decision Entropy (CDE) both metric and re- ward for reasoning quality. Across three hateful meme benchmarks, ExPO-HM achieves state-of-the-are performance binary detection, fine-graines classifi- cation, and reasoning quality, with up to 15% and 17% F1 improvement over the GRPO and DPO baselines, respectively. By moving hateful meme detection from simple binary alarms to explanation-driven detection, EXPO-HM provides accurate, interpretable, and actionable moderation support. Code available at: http://giiibboooiininaaa/aa/////
This paper contains content for demonstration purposes that may be disturbing for some readers.
INTRODUCTION
The rise of social media has led surge in hateful content, notably in the form of memes. This has sparked growing research interest in automated hateful meme detection systems that aim at sup- porting human moderation (Kiela et al. 2020 Liu al.) 2022; Prakash al.) 2023 Shah et al. 2024). Most prior work focuses on direct detection, which only provides binary classification as to whether meme is hateful or benign (Cao et al. 2023, Mei al. 2024 Su al. 2025). However,
recent studies show that moderators require additional information to improve effiiency Calabrese al. 2024). such as what type of attack present, and why the system considers the meme harm- ful. Additionally, social media users may also benefit from understanding these explanations of harmfulness.
Interestingly, human annotators are not trained and evaluated on binary judgments; common practice is that they are guided by a detailed moderation policy manual that defines policy violations such as
"Wok done during internship Xiaohongshu Inc
Equal Contribution
1 "Corrsponndig authors

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Published as a conference paper at ICLR 2026
Direct Detection Methods
Input
large-
Previous then Detect Methods
Tool Rutrieval
Chain of Thought (CoT) prompting
Agent-based Framework
F
ExPO-HM
D
(Ours)
Policy
Curriculum Arrangement
Practice Reinforcement
Redesign Rewards
Figure 1: Comparing previous methods with ExPO-HM.
disparagement of protected groups (Singhal al. 2023). It would be infeasible to train annotators by showing them only raw examples with binary labels; the fine-grained framework provides the necessary structure for both training and evaluation. This human analogy highlights crucial gap: humans require fine-grained guidelines and reasoning to make reliable judgments, automated systems could benefit from the same. We call this setting "Explain- thee--hhe---e -hen--eteen""" Detect", where the system first generates a natural language rationale and then produces classification decision.
Recent work builds Explain-then-Detee Large Multimodal Model (LMM) systems using Chain-of- Thought (CoT) prompting (Wei et al., 2023 Pan et al. 2025) or agent-based frameworks (Huang al. 2024), but these perform worse than direct Supervised Fine-tuning (SFT) baselines (Mei al.,
2025 Reinforcement learning methods such as Group-Relative Policy Optimization (GRPO) (Shao
al. 2024) can strengthen model reasoning through post-training, yet we find that applying GRPO directly still underperforms SFT for hateful meme detection. Our study reveals two key challenges for Explain-then-Deter systems. First, model explanations often fail to identify the correct violated policy target, leading to misleading predictions. Second, the binary reward signal in GRPO is too weak to guide reasoning, just as human annotators cannot learn from only yes/no labels.
To address these issues, propose ExPO-HM
Policy Optimization for Hateful Memes), inspired by how human annotators are trained and evaluated. EXPO-HM first SFT warmup on policy manual, mirroring the guideline-based training of human annotators. We then apply GRPO with curriculum learning, mimicking how annotators are first trained and eval- uated on fine-gras ame-grained categories before making binary judgments. We further introduce Conditional Decision Entropy (CDE) both as metric for explanation quality and as a reward signal to encourage decisive reasoning. We summarize our contributions:
Paradigm. We introduce the first Explain- -eplain-then--ae Deeect hateful meme detection that outper- forms direct detection, enabling accurate and interpretable hateful meme understanding.
Methods. ExPO-HM mimics human moderator training, combining policy manual SFT warmup, GRPO curriculum learning, and CDE-based reward optimization.
Evaluation. We propose comprehensive evaluation setup that reflects sal-world modera- tion, extending beyond binary classification to fine-grained categories and hateful reasoning judged by L.Mss, with extensive baseline comparisons.
Results. ExPO-HM surpasses previous best systems, and achieves state-of-the-an performance across binary, fine-grained, and reasoning benchmarks, with up to 15 % and 17% F1 improvement over the GRPO and DPO baseline, respectively.
RELATED WORK
Direct Hateful Meme Detection Most existing approaches to hateful meme detection treat the task as binary classification. Numerous studies fine-tune CLIP-based models using only binary labels and train dedicated classifiers (Pramanick al. 2021 Kumar & Nandakumar 2022 Burbi
et al. 2023 Cao et al. 2023 Ji et al.] 20244 Mei et al.] (024). Decoder-based LMMs have also been
fine-tuned for this task (Alayrac et al. 2022 Laurençon al. 2023 Hu al. 2024). In particular, Mei et al. (2025) trains a classifier and retriever on top of the LMM embeddings, achieving state-of- the-art binary detection performance.
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In contrast, fine-grained classification, such as identifying attack types or target groups, has received far less attention, despite its importance in real-world moderation. Annotated datasets are avail- able (Mathias et al., 202 Dimitrov al., [021
Fersini al. 2022 Shah et al. 2024), and some earlier work has explored this problem (Zia et al.
2021, Mathias et al. b021 but recent progress and IntMeme (Hee & Lee 2025) leverage fine- grained annotations during training but do not report fine-grained results. MemeCLIP (Shah et al.) 2024) addresses this by fine-tuning separate CLIP-based classifiers for each split. In this paper, we systematically evaluate models under different setups and extend the evaluation to fine-graine classification, addressing this important gap.
has been limited. Mod-Hate (Cao al., 2024)
Explain-then-Dette DDtect Hateful Meme Detection Compared to direct hateful meme classification, research on explainable hateful meme detection is far more limited. With the rise of decoder- based language models, some Explain-then-Detee
systems have emerged. For example, Lin et al. (2024) leverages a debate between two language models to decide meme harmfulness, while LOREHM (Huang et al.) 2024) adopts a reasoning-agen framework with retrieval and reflection. However, these systems still primarily target binary classification.
A key challenge is the lack annotated explanation data. Hatred (Hee et al. 2023), built on the Facebook Hateful Memes dataset (Kiela et al. 2020), remains the only open-source dataset with human-writer written rationales. Other efforts, such as the recent Arabic hateful meme dataset ArMeme (Kmainasi et al.) (025), not yet publicty available. Moreover, reasoning tasks systems not only struggle with reasoning but also underperform direct detection models in binary classification, underscoring the cost of requiring explanations without tailored optimization strategies. In this paper, we make two key contributions. First, we benchmark comprehensive set of Explain-then-Detee the Hatred dataset. Second, inspired by human moderator training, we develop ExPO-HM, the first Explain-then theeet system that surpasses both prior explainable and direct detection approaches, delivering accurate and interpretable hateful meme detection.
main difficult (Nguyen & Ng (024).. Existing Explain-then-Deeee
systems using
EXPO-HM METHODOLOGY
3.1 PRELIMINARIES
Problem Statement. A common binary hateful memes classification dataset (Kiela al. 2020)
is D = {44, ct) where 4 E RC is an image with overlaid text (C for channels, H for height, W for width), and the ground-truth label og E {0, denotes benign hateful. In addition, we consider annotations including fine-grained labels 2 (e.g.. protected category, attack type) (Mathias et al. 2021a) and, when available, gold explanations (Hee et al., 2023) c,- We thus define the three tasks for hateful meme detection: (1) predicting binary class c;; (2) predicting fine-grained class z; (3) generating e;, For text-based evaluation, we denote the textualized label prediction as d; (from or z;) and the corresponding ground-truth text label as d'.
Large Multimodal Models (LMMs). Given a meme I and prompt p. we denote the input to LMM (I, An LMM with parameters defines an auto-regressive policy over output text tokens = (31..... Myy))
|yl I
T xx)
(ye 1..t,,) x),
(I)
where indexes the output tokens. Direct- Detee--eteetoon methods decode labels directly, via answers like "yes"" / "no" (Lin et al. 2024). In contrast, Explain-then-Dee first generates reasoning and then the label. Following the standard long CoT format (DeepSeek-A) Al et al. 2025), the output sequence
is:
y = (<think> e </think> <answer> d </answer>),
(2)
where e is the generated explanation and d is the textualized label prediction.
Supervised Fine-Tuning (SFT). Given an input x and a target output sequence y", the model is trained by maximizing the likelihood of y":
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1y
LSFT (0)
log no To (v yx
(3)
This serves as the general form of SFT used in our baselines.
Direct Preference Optimization (DPO). We consider DPO (Rafailov al. 2023) as a baseline fine-tuning method. Preference pairs yy* y) are sampled on-policy from the reference model ref via the Explain-thers then-Detect prompting format. A response y is selected as the preferred response y" its decision matches the ground-truth label d"; otherwise, is treated as the rejected response y
We optimize the DPO objective:
(0) log a (Blog T (x)
To (x)
8 log Tef (y
Teet (yer((+(x))
where is the sigmoid function and ree is the reference model, ie.,, the initial model before DPO fine-tuning.
Group Relative Policy Optimization (GRPO). GRPO (Shao al. ent method that discards the critic model to save computation. To estimate the advantage, samples group of outputs (Y1:-...Yo from the old policy Teas for each input The advantage for the g-th sample in a group is computed by normalizing its reward against the group's 's reward distribution (GG)
2024 is online Policy Gradi-
rg mcan (mrannfri....,rae rG))
Ag
We consider verifiable reward functions in this paper. The policy is then optimized with the clipped objective:
To (y:s (x) THu x)
To(y; 1-E, E, 1+t) A, -3DKI (Tef)))ee) To (y:
(0)
min
Ai, clip
3.2 CONDITIONAL DECISION ENTROPY
The reasoning quality is difficult to optimize in hateful meme detection, as there is no reliable reward model due to the scarce rationale corpora and subjective human judgements. To address this, we propose Conditional Decision Entropy (CDE) as a proxy measure. The principle of CDE straightforward: good reasoning should lead sharp and correct decision, while poor reasoning produces confusion.
CDE Definition. For an input x, the LMM To generates an explanation and decision response (c, d) ~ To(- x) in the format of Eq, 2 where the final decision is sampled conditioned on the explanation and input ne(- e, x). We define CDE as the entropy the decision conditioned on the produced explanation:
H(d e, x) = -. log (od e, x)].
Monte Carlo Estimator for CDE To evaluate reasoning quality with CDE, we estimate the aver- age CDE over the validation set. For each example x,, we sample K = 16 explanations e, with the policy me and compute the entropy of the decision distribution. The estimator is
K ΣΣ
P
H(d eik, es me(- x.).
(8)
K
In the binary classification case, we experimented with collapsing the decision vocabulary to V (Yes,No), making CDE equivalent to binary entropy. We observed no significant differ- ence compared to using the full vocabulary. For generalizability to fine-grained multi-class labels, therefore adopt the full vocabulary formulation. full derivation is provided in Appendix G
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Policy Manuel
@SFT-PM Warmup
Religion: A group defined by shared belief system:
Input
Cutput
Input (L.
Contempt: Expressing strong disgust. hatred, or intense nagative emation toward group
LMM
Sturs: Using prejudicial derogatory terms to describe refer to group
Fine-grained Class
Ry <thinks The
racial stereatype by equating people to brown sugar, which is a dehunanizing comparison. The use of the tem ("brown sugar)" is racial slun- c/thinks
B
Sampling
LMM
IISTERIT NOWISS
Y(g
CL-Schedkller
G=8 dh canswers Racist, </answor>
FG: Which policy does violate? (o)... (B) -
Training Steps
Forrsnt Fl 42)
Binary: ls hatoful?
Yepe f 41]
@ GRPO with Curriculum Learning
GD GRPO with CDE Reward
Figure 2: Architecture of ExPO-HM. Our framework consists of three key components: ① SFT-PM Warmup. The VLM is first trained with SFT using structured policy manuals derived from fine- grained labels and dataset guidelines, teaching the model to align decisions with explicit moderation policies. 2 GRPO with Curriculum Learning. Training follows a two-stage schedule: the first 50% of steps use fine-gre ane-ggrined data only for reasoning exploration. and the remaining 50% use a balanced 50/50 mix of fine-grained and binary data. 33 GRPO with CDE reward. In addition to the format reward (Forma) and accuracy (Tace) reward used in standard GRPO, we also add a Conditional Decision Entropy (FCDE) reward.
3.3 EXPO-HM FRAMEWORK
Inspired by human moderator training, where annotators first study annotation guidelines and then practice applying them to tasks of increasing difficulty, ExPO-HM, as shown Figure first learn policy knowledge through SFT, then refines its reasoning via GRPO with curriculum learning, pro- gressing from fine-grained to binary classification.
SET Warmup on Struetured Policy Manuals (SFT-PM). We first teach the LMM moderation policy knowledge by converting each datasct's fine-grained labels into a structured policy manual as the input prompt. Descriptions derived from the dataset annotation guidelines are added to each policy item in the policy manual. Details of this conversion process are provided in Appendix B.2 We optimize the language modelling loss in 3 with this policy manual augmented input for each meme, and the target response y" is the fine-grained label dñ. Note that we do not use human- written gold hateful explanation ex in the warmup stage, they off-policy and lead worse performance, which we discuss in Sec 4.5
GRPO with Curriculum Learning (GRPO-CL). After the SFT-PM warmup, we conduct GRPO curriculum learning. We begin with fine-grained classification to incentivize policy understanding through diverse reasoning exploration, then introduce binary classification for hateful vs. benign detection. We test various curriculum schedulers, switching after fine- seee-graiedd accuracy plateaus, adjusting the budget split between stages, or adjusting the mixing ratio of the fine-grained and binary data in the second stage, and find similar performance as long fine-grained reasoning precedes binary. We therefore adopt a simple 50/50/50 strategy: the first 50% of steps use fine- ane-grained data only, and the remaining 50% a balanced 50/50 mix of fine-grat fine-grained and binary data.
We optimize the clipped surrogate loss in Eq. 6 using the group-relative advantage Eq. 5 The reward Tig corresponds to the g-th response in the sampled group for the i-th training example
r(yig, d)) Tformat + Tace + WCDE,
where Tformat E 1) checks if the output obeys the correct template in Eq. 2 The accuracy reward Face E [0. 1] measures prediction correctness with partial credit for multi-clase fine- tineggaiiee classification and penalties for over- ever-prediction For binary classification, it requires exact match and thus Tace E {0. 1). For the GRPO baseline, we set w 0, leaving only the format and accuracy rewards. Now let's define CDE Reward TCDE-
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CDE as Reward Although GRPO with curriculum learning improves the naive GRPO baseline, still falls short in producing reliable reasoning. As introduced in Sec. 3.2 CDE provides proxy for reasoning quality. If the prediction is sharp and correct, the reasoning is helpful and should be rewarded; if is wrong but confident, the reasoning misleading and should be penalized. We therefore incorporate it additional reward to guide ExPO-HM.
For each group-sampled example Yig of each input x,, we denote the CDE as hig and correctness as òigi
hig H Cigy x1);
8ig 1[dg =d]
(10)
We reward confident correctness, tolerate uncertainty when wrong, and penalize confident errors. The CDE reward for the example Yig is
w b
h hsa
pw:
hig
hig
hig
(CDE(hig. 0ig) = dig
w
< hig < + (1 (1-dig)
b a < hig < b (11)
b
0,
hig 2
we
hig
CDE rewards contribute a maximum of weight w, with p controlling the penalty strength for over- confident wrong predictions. Unless otherwise noted, we default hyperparameters:
0.1.
b = 0.5, w = 0.2, and 0.25. A detailed hyperparameter analysis is provided in Appendix C.3 The TCDE can thus be fed into Eq. 9 to obtain the reward to compute advantage optimize the GRPO objective.
4 EXPERIMENTS
4.1
EXPERIMENTAL SETUP
Dataset. We evaluate the binary and fine-grained classification on three meme classification datasets: HatefulMemes (Kiela et al. 2020), MAMI (Fersini et al. 2022), and PrideMM (Shah) et al. 2024).
Tasks We evaluate binary classification (hateful vs benign) on all three datasets. For fine-grained classification, we assess attack methods and target groups on HatefulMemes, attack methods on MAMI, and stance towards LGBTQ+, along with target group detection on PrideMM. Due to the scarcity of annotated hate rationales, we only evaluate reasoning quality on HatefulMemes, where gold human rationales are available (Hee et al. 2023). Detailed dataset descriptions and statistics provided in Appendix B
Evaluation Metrics. We evaluate classification tasks using macro F1 following prior work (Shah al. 2024). For fine-grained classification, we use micro F1 due a highly imbalanced class distribution. For reasoning quality, we adopt the LLM-as-a-judge method (Yang et al., 2023 Mei jet al. 2025) to measure alignment between model-generated and human rationales. The detailed evaluation setup is provided in Appendix D We further include different LLM judge experiments in Appendix E In addition, we report CDE as a proxy to reasoning quality and verify its correlation We also report human evaluation of the reasoning in Section
with LLM-as-a-jjuge in Section 4.3
4.2 BASELINES
We compare ExPO-HM with comprehensive baselines D and Qwen2.5-VL VL- 7B (Bai et al. 2025) in Table T In this section, we describe the baseline setup briefly. Full im- plementation details are provided in Appendix Co ensure reproducibility.
SFT. In this paper, we consider two variants SFT as baselines. Direct-SFT trained with the ground-truth label as the target (y* = d" in 3). while CoT-SFT uses Explain-then-Detee prompt adopted in DPO and GRPO, where the target sequence is the chosen response DPO sampling (y* = in Eq. 3. In practice, when inference is performed with the Explain-ther seeaain-then-Dee then-Detect prompt. We therefore report Direct-SFT the default baseline. For classification, train and report separate models based the binary
find that Direct-SFT consistently outperforms CoT-SFT,

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and the fine-grained subset, and report the best results. Full results for each model are provided in Table 3 while Table reports the best system.
DPO & GRPO. For DPO and GRPO, initialize from the fine-grained SFT warmup, but with- out the policy-manual style augmentation. We sweep different values in DPO to get the best performance the validation set. The GRPO baseline is trained with the same compute budget as ExPO-HM, using identical hyperparameter settings both the warmup and GRPO fine-tuning stages.
Best prior systems. We compare EXPO-HM with the best prior systems. RA-HMD Mei et al. 2025 is the state-of-the-art direct detection model, combining two-stage fine-tuning and retrieval- augmented classification. Although primarily designed for direct detection, it supports reasoning evaluation via prompting, so we report its LLM--a-aajudge scores. All RA-HMD results are based on 2wen2..5VLL-7 (Bai et al., 2025). For reflective reasoning agent with tool-calling capability built on LLaVA-Next-34B (Liu et al. 20024) and U-CoT+ (Pan al. 2025), which uses human-guided CoT prompting with Owen2-VL-7B (Wang al., 2024a) for meme-to-text conversion and Qwen2.5- 14B (Qwen et al. 2025) for answer generation. We can only report their results on the binary classification due to their prompt- agent design, these systems cannot directly adapted for structured reasoning tasks. Furthermore,
Seeiin-then-Deteeee we compare two recent systems:
LOREHM (Huang et al. 2024),
fine-grained classification source reasoning LMMs such as the OpenAl o-series (OpenAl 2024) as baselines, since over 30% of requests were blocked by the API server due the harmful nature of the examples.
did not include closed-
Table I: Comparing ExPO-HM with baseline systems across three datasets. stands for Binary and R stands for Reasoning. LLM refers to the LLMe-a---judge score. Best results are in bold. indicates higher is better, | lower is better.
MatefulMemes Target
MAMI Attack
PrideMM
# Model
B
Attack
R FI L
B
R
B
Stance Target R CDE
F
FIf
FI
FI 1 CDE
FI
FIT
Direct Detection Baselines
1
Qwen2.5-VL-3B
2
Zero-shot
53.1
42.1
60.1
61.1
48.2
58.6
53.7
48.8
3
SFT
71.9
64.3
69.3
77.9
61.8
74.3
58.6
53.2
4
Qwen2.5-VL-7B
5.
Zero-shot
59.8
50.3
60.2
63.4
50.2
65.2
56.8
51.1
SFT
64.7
71.1
78.1
63.1
75.6
60.2
61.0
7 RA-HMD
80.2
5.5
81.0
77.8
Explain-then-Dere Systems
8
LOREHM (34B)
65.6
75.3
9 U-CoT+ (14B)
72.4
79.9
71.4
10 Qwen2.5-VL-3B
11 Zero-shot
52.5
41.7
58.7
3.3
0.42
58.7
41.7
0.32
52.6
51.2
40.8
0.33
12 SFT
63.3
0.40
69.2
60.1
0.34
49.8
13 DPO
59.6
52.3
58.1
3.5
0.42
66.8
50.2
0.36
64.2
55.5
48.9
0.34
14 GRPO
63.4
55.6
66.1
3.8
0.32
76.6
61.2
0.19
72.1
57.3
48.4
0.18
15 EXPO-HM
74.7
71.5
73.7
5.1
0.16
80.7
70.4
0.08
75.6
66.5
62.1
0.12
16 Qwen2.5-VI--7B
17 Zero-shot
65.9
44.7
64.5
5.0
0.33
63.9
46.5
0.23
59.4
$4.6
50.2
0.28
18 SFT
74.5
58.4
69.4
0.33
72.8
62.6
0.19
68.3
58.0
50.9
0.28
73.6
66.6
4.9
0.32
72.3
56.6
0.22
$2.3
20 GRPO
74.5
61.2
64.5
5.2
0.26
76.8
63.7
0.09
73.2
58.6
60.1
0.14
21 EXPO-IIM
81.1
75.6
77.2
6.2
0.03
82.3
73.0
0.04
78.7
68.4
65.1
0.08
4.3 COMPARING EXPO-HM TO BASELINE SYSTEMS
Table I compares EXPO-HM with the aforementioned baseline post-training methods and state-of- the-art systems. We report qualitative examples and error cases Appendix J Here, we summarize the key observations.

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Baseline Explain-then-Detee methods hurt classification performance. Under Explain-ther Detect, post- training variants (SFT/DPO/GRPO, #18-#20 for Qwen2.5-VL-7B) consistently under- perform the Direct-Detection SFT baseline (#6), except for the comparable performance MAMI Attack classification. Larger agentic and CoT systems (LOREHH,, U-CoTT+ #8-#9) also fall short of strong Direct-Detectinn baselines like SFT and RA-HMD (#6-#7 ). For instance, the binary classification on HatefulMemes is 80.2 on RA-HMD vs 72.4 with U-CoT+. On Hateful- Memes binary classification, RA-HMD reaches 80.2 FI, compared to 72.4 with U-CoT+. Explain- then-Detect systems are crucial for building automatic moderation systems that can truly support real-world moderators, but these results highlight that simply adding explicit rationales through CoT prompting or standard post-training hurts classification accuracy. This motivates the design of EXPO-HM, which aims to improve Explain-then-Deee systems without sacrificing predictive performance.
the
Naive post-training barely improves performance. Explain- theninn-tee--aee then-Detect post-training (+18-#20) improves classification over zero-shot (#17), but reasoning quality stagnates, failing meet the goal of improving reasoning through post-training. On HatefulMemes with Qwen2.5-VL-7B, the zero- shot LLM-aa-aajjdge score is 5.0; DPO drops below this, while GRPO only nudges it to 5.2. Even with online RL, reasoning remains difficult to improve. Moreover, post- training still underperforms strong CoT systems specifically designed for hateful meme detection (#8-#9). This underscores the need for dedicated post-training methods like EXPO-HM, which are tailored to hateful meme de- tection and designed improve not only classification accuracy but also the quality of explanations.
EXPO-HM consistently outperforms. EXPO-HM delivers the strongest performance across binary detection, fine-grained classification, and reasoning. On Qwen2.5-VL-7B, it surpasses RA-HMD and all post-training baselines, achieving large gains in fine-grained F1 (+14.4 on HatefulMemes At- tack, +12.7 on Target, compared GRPO with equal compute). Reasoning also improves markedly, with 6.2 on LLM-as-a-judge vs. 5.2 for GRPO. In Appendix [ we conduct additional evaluations using different L.M judges and paraphrased prompts, and ExPO-HM consistently outperforms all baselines under all settings. Appendix [ further reports per-class metrics for Attack and Target de- tection on HatefulMemes, showing that EXPO-HM achieves consistent improvements, particularly on the most challenging categories. These results confirm ExPO-HM's effectiveness across datasets and tasks.
Strong correlation between LLM-as-a-judge metric and CDE metric. On the HatefulMemes reasoning dataset, we observe a strong alignment between the LLM- -as--udu-d score and the CDE score. To quantify this, evaluate the correlation based on results from all the reported setups, with three random seeds each, yielding 60 data points. We find strong negative correlation (Pearson -0.78, Spearman م -0.81, both p 0.001), confirming that lower CDE values, reflecting more confident and accurate reasoning, correspond to higher reasoning quality.
4.4 ABLATION STUDY OF EXPO-HM COMPONENTS
We conduct an ablation study to examine the con-
Table 2: Ablation Study of ExPO-HM Components.
tribution of the three key components in ExPO-HM. Results on HetefulMemms with Owen2.5-VL-7B are reported in Table With- out SFT-PM, the warmup falls back to SFT with fine- grained labels without pol- icy manual augmentation. Without GRPO-CL, GRPO
Components SFT-PM GRPO-CL CDE
HatefulMemes
Attack
Target Fl
R
F1 t
F1 f
LLM f
CDE 4
74.5
61.2
64.5
5.2
0.263
75.8
70.8
70.2
5.6
0.092
V
78.4
74.3
76.1
5.8
0.056
V
81.1
75.6
77.2
6.2
0.026
is trained on randomly mixed set of binary and fine-grained data. Without CDE, GRPO uses only the format and accu- racy rewards.
SFT-PM enhanced the fine-grained warmup. Compared to the baseline warmup without policy manual augmentation (#1), SFT-PM improves performance across all metrics. This indicates that
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Table 3: Comparing SFT warmup variants on HatefulMemes on Qwen2.5-VL-7B: no warmup (-),
SFT on binary labels (SFT-B), SFT on gold reasoning (SFT-R), SFT on fine-grained labels (SFT- FG), and SFT with policy-manua augmentation (SFT-PM).
SFT
w/ GRPO-CL and CDE
Warmup
B
Attack
Target F1
R
Attack F1
Target F1 f
R
F1 f
F1 f
LLM f
FIT T
LLM
f
65.9
44.7
64.5
5.0
73.3
69.3
72.1
5.2
SFT-B
74.1
58.2
69.4
4.9
73.5
66.8
70.1
5.1
SFT-R
72.2
51.6
63.1
5.0
79.2
72.3
73.2
5.7
4
SFT-FG
72.5
58.4
67.7
4.9
78.9
73.4
73.4
5.6
SFT-PM
74.3
64.6
68.8
5.0
81.1
75.6
77.2
6.2
fine-graine sine-graieed labels alone insufficient for policy understanding, while policy manual augmenta- tion substantially strengthens both classification and reasoning. In Sec. 4.5 we further present systematic comparison of different warmup strategies.
GRPO-CL further improves performance. Building on SFT-PM, adding curriculum learning to GRPO (#3) yields further gains across the board. The key difference ordering, GRPO-CL first lets the model explore reasoning over the fine-grained labels before binary classification. This order proves crucial: standard GRPO produces short average responses (28 tokens) in binary elassification, while GRPO-CL nearly doubles this (52 tokens), indicating not only higher quality but also more detailed reasoning is incentivized during training.
CDE improves both accuracy and explanation quality. Adding CDE top of SFT-PM and GRPO-CL further improves the performance. Notably, LLM-judge score improved to 6.2, and marked drop in CDE 0.026, suggesting that the model's 's rationales become more aligned with sharp, correct decisions.
4.5 EFFECTS OF DIFFERENT WARMUP STRATEGY
Table compares five warmup strategies for Qwen2.5-VL-7B the HatefulMemes dataset. For each, we report the Deolain-then-Deteeee performance after SFT and the performance after GRPO-
CL with CDE reward.
Good SFT does not necessarily transfer to good RL performance. Although SFT-B performs better than SFT-R and SFT-FG at the SFT stage, its performance after GRPO-CL is comparably worse than its counterparts, even below the no- no-warmup baseline. This suggests that binary-only warmup fails equip the model with the moderation concepts needed for reasoning-guided RL. In contrast, our proposed SFT-PM explicitly teaches such concepts via policy manual augmentation, yielding both stronger SFT performance and the best results after EXPO-HM training.
4.6 CDE ANALYSIS
Conect Predictions
Wirong Predictions
Conditional Decision Entropy
0.4
0.2
ExPO Coreec (..0.019)
ExPO Whong (um-0 (um00.000
Correct (pm00.27) Wrong (u-00220
Figure 3: Comparison of CDE distributions between ExPO-HM and GRPO on the Memess validation set with Qwen2.5-VL-7B.

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CDE Distribution Analysis. Figure presents wox-ske-whisker plots of CDE distributions for ExPO-HM and GRPO on the HatefulMemes validation set with Qwen2.5-VL-7B EXPO-HM main- tains very low CDE for correct predictions (μ 0.019) and higher CDE for wrong ones (μ 0.048), yielding a clear separation. In contrast, the GRPO baseline shows high CDE for both correct (4 = 0.278) and wrong predictions (1 0. 0.) showing weaker separation. This demonstrates that EXPO-HM produces reasoning that is not only more accurate but also better aligned with decision confidence.
CDE for Decision Calibration. The CDE reward penalizes confident wrong predictions and re- wards confident correct decisions. This encourages a well-hehaved decision distribution: the model becomes confident only when it is likely to be correct, and becomes uncertain when the outcome is ambiguous.
To validate whether CDE reward improves calibration, we compute Expected Calibration Error (ECE) and Brier score using the model's probability assigned to the final answer token, condi- tioned on the generated explanation under the Explain-then then-Detect setup. We observe that ExPO- HM consistently improves calibration compared to the GRPO baseline. Notably, for Qwen2.5-VL. 3B, ExPO-HM reduces the Brier score from 0.590 to 0.283, indicating substantially more reliable decision confidence. Full calibration results are reported in Appendix [H.T] We additionally provide derivation of the upper bound on the Brier score under the ideal ExPO-HM policy Appendix H..
CDE and Policy Entropy. We test whether adding the CDE reward causes policy entropy col- lapse, a phenomenon reported prior RI. work (Cui et al. 2025) when entropy bonuses are re- moved. Our results show that overall policy entropy remains comparable to the baseline GRPO system without CDE, confirming that the CDE reward, acting only on the decision part of the gen- eration, does not reduce exploration.
CDE Hyper pepepaarameeers. For CDE hyperparameters, we conduct standard hyperparameter tuning via grid search the MatefulMemes validation Once the optimal values were identified, fixed these parameters and applied them directly to MAMI and PrideMM. We observe that as long as the hyperparameters fall within reasonable range, the model performance remains highly stable. We provide the detailed insights of hyperparameter tuning in Appendix C.3
4.7 HUMAN EVALUATION OF MODEL-GENERAN REASONING
To assess the quality of the generated reasoning beyond L.MM-s--a M-aas---juddee evaluation, we further conduct two complementary human evaluations. Each example is independently evaluated by three crowd-sourced annotators with at least an undergraduate degree and demonstrated familiarity with internet meme culture. We evaluate both the GRPO baseline and our ExPO-HM model. The detailed evaluation setup and full results are provided in Appendix
Coherence Evaluation. Annotators judge whether the model's final decision is logically sup- ported by its rationale. The GRPO baseline achieves 96% coherent outputs, whereas EXPO-HM attains 100% coherence.
Helpfulness Evaluation. Annotators also rate how helpful each rationale is for understanding why the meme hateful or benign, using a 0-4 Likert seale following prior work (Wang et al, 2024b). We obtain average helpfulness scores of 1.6 for GRPO and 2.2 for EXPO-HM. After nor- malizing the scores to the same 0-10 scale used by the LLM-as-a-judge (4.1 vs. 5,5), we observe high agreement between human and LLM evaluations in the relative improvement from GRPO to ExPO-HM.
5 CONCLUSION
We propose ExPO-HM, which combines SET warmup on manual-augmentte data with GRPO curriculum learning, guided by Conditional Decision Entropy reward to promote high- quality reasoning. Comprehensive experiments show that ExPO-HM achieves ofatc-offthh-ar per- formance on binary detection, fine-grained classification, and reasoning quality.
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ETHICAL STATEMENT
Societal benefits. Hateful meme detection systems such as ExPO-HM can help automatically identify and mitigate harmful online content, reducing the prevalence of hate speech. By provid- ing explanations in addition to predictions, our system not only supports safer digital environments for end-users but also alleviates the burden on human content moderators, improving their well- being. We believe such systems play an essential role in fostering respectful online communication and contributing healthier digital communities.
Intended use. We will enforce strict access controls for releasing model checkpoints and artifacts. Access will be limited to researchers who agree to our terms use, which explicitly restrict the system to the detection and prevention of hateful speech. Any use that promotes, condones, or encourages hate speech or other harmful content is strictly prohibited.
Misuse potential. Although ExPO-HM is not designed introduce bias, is trained on datasets that may reflect societal or annotator biases (Pramanick et al. (021). These biases could propagate into model predictions. To mitigate risks of unfair or disproportionate moderation, human oversight remains essential when deploying such systems.
Deployment considerations. Moderation hateful content inherently influenced by cultural norms and subjective judgments. Expressions considered benign in one context may be offensive in another. Since ExPO-HM is trained with policy manuals, its outputs depend critically on the underlying moderation policies. Careful review and adaptation of community guidelines are crucial to ensure responsible deployment across diverse cultural and linguistic contexts.
Usage of Datasets. The datasets used in this study, HatefulMemes, MAMI, and PrideMM, were curated for research purposes to combat online hate speech. We strictly adhere to the terms of use established by the dataset authors.
ACKNOWLEDGMENTS
Jingbiao Mei is supported by the Cambridge Commonwealth, European and International Trust for his PhD studies in Engineering at the University of Cambridge. This work was conducted during an internship at Xiaohongshu Inc.
Jinghong Chen is supported by the Warwick Postgraduate Studentship from Christ's College and the Huawei Hisilicon Studentship for the undertaking of the PhD Engineering at the University of Cambridge.
Prof. Bill Byrne holds concurrent appointments as a Professor of Information Engineering at Cam- bridge University and as an Amazon Scholar. This publication describes work performed at Cam- bridge University and not associated with Amazon.
We would also like to thank all the reviewers for their knowledgeable reviews.
REPRODUCIBILITY STATEMENT
We provide detailed dataset usage and statistics in Appendix B including all augmentation proce- dures introduced in this paper. For the evaluation of reasoning, we detail the process in Appendix Experimental setups, implementation details including code base usage, software version, hardware configuration, and hyperparameter settings are described in Appendix CC to facilitate reproducibility. Code and data available: https//gghhbb..omm//mmmaa/M/a//a
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A USE OF LARGE LANGUAGE MODELS
Our coding work was assisted by Github Copilot. OpenAl ChatGPT was only used for proofreading and spell-checking. We claim that the content presented in this paper was fully original.
B DATASET DETAILS AND STATISTICS
B.1 DATASET DETAILS AND PROMPT USED
HatefulMemes. Released by Meta in 2020, HatefulMemes (Kiela et al. (020) contains 12,000 memes annotated as hateful benign by trained experts. This benchmark dataset synthesizes memes targeting religion, race, disability, and gender. It includes confounder examples where the benign memes are generated by altering either the image or text to challenge models' ability multimodal reasoning.
In 2021, Meta further released an extended annotation set, the Fine-Grained Hateful Meme Clas- sification dataset (Mathias et al., 2021a). This dataset augments the original HatefulMemes with detailed labels for both attack types and target groups, enabling fine- gr-igrained classification.
For fine- following prompts for atlack types:
fine-grained grained classification training and evaluation, except for the SFT-PM training, adopt the
Does this meme express hate or negative bias toward any of the following protected groups? Religion, Race, Sex, Nationality, Disability Please respond with more of the protected categories if applicable. If the does contain any hateful content, respond with Benign.
For target groups:
Does this meme use any of the following types of attack against a group? Dehumanizing, Inferiority, Inciting violence, Macking, Contempt, Slurs, Exclusion Please respond with one or more of the attack types if applicable. If the meme does not contain any hateful content, respond with Benign.
MAMI. The MAMI dataset (Fersini et al. 2022) focuses on detecting misogynistic memes sourced from various social media platforms, including Twitter and Reddit, well meme creation and sharing websites, and even anti-women websites and forums. It contains annotations for two tasks: (I) binary classification of misogyny and (2) categorization of misogyny types. In this work, we use the binary task to detect whether meme is misogynistic and treat the type categorization as fine fineggriiedd multi-class, multi-label classification problem. Each sample can take one or more of four attack type labels: objectification, shaming, stereotype, and violence, or Benign if no harm is present.
For all fine-grained training and evaluation tasks on fine-grained classes except for the SFT-PM training, we use the prompt:
Does this meme use any following types attack against group? objectification, shaming, stereotype, violence Please respond with one more of the attack types if applicable. the meme does not contain any hateful content, respond with Benign.
PrideMM. PrideMM (Shah et al., 2024) contains LGBTQ+ themed memes annotated for four tasks: hate speech detection, hate target identification. topical stance classification, and humor de- tection. this work, we use the hate speech classification annotations for the binary hateful meme detection.
We further use the hate target identification and topical stance classification in our fine- Ene-grained classification setup. Both tasks are formulated as multi-class, single-label classification.
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For all fine-graine grained training and evaluation tasks except for the SFT-PM training, the Target identification prompt:
Based on the content and context of this meme, does this meme express hate or negative bias toward any of the following targets?
Choose from the following options: undirected individual community organization
If the meme does not contain any hateful content, respond with Benign. What the target type?
Stance classification prompt:
Based on the content and context of this meme, what the stance towards LGBTQ+ individuals or communities?
Choosc from the following options: neutral, support, oppose
What the stance?
Explain-Then-De Detect Prompt. The above shows the prompt used for the direct detection setup. For the Explain- Explain-Then-Bee Then-Deter Detect Prompt, we adapt the prompt from the VeRL training library:
<BASE.PROMPT> Output the thinking process in <think> </think> and final answer in <answer> </answer> tags. The output format should be follows: <think> ... </think> <answer>...</nnde annswor>> Please strictly follow the format.
The <BASE. PROMPT> replaced with the specific prompts defined above.
B.2 POLICY MANUAL CONSTRUCTION
For HatefulMemes, MAMI, and PrideMM, the dataset authors provide detailed annotation guide- lines in prose, including lists of protected offensive categories (c.g., ethnicity, race, violence) and their definitions. We extract this information and convert it into a concise bullet-point list, which we refer as the policy manual. Below is example of the original annotation guideline from (Kiela et al. 2020):
"A direct or indirect attack on people based on characteristics, including ethnicity. race, nationality.. We define attack as violent or dehumanizing (comparing people
to non-human things, e.g. animals) speech, statements of inferiority.."
Representing the annotation guidelines in the structured Policy Manual form makes it easier to create targeted instruction-followin SFT data compared to using long-form prose descriptions. The conversion from guideline policy manual is one-time process performed by human expert. Below, we detail the specific policy manuals for each dataset.
HatefulMemes. For policy-manual SFT, we use the following prompts:
Does this meme use any of the following types of attack against a group?
Dehumanizing: Presenting group subhuman, explicitly or implicitly
Inferiority: Claiming that group is inferior, less worthy, less important
Inciting violence: Calling or encouraging harm ceem-physccal or otherwise towards a group
Macking: Belittling or making jokes about group
Contempt: Expressing strong disgust, hatred, or intense negative emotion
Slurs: Using prejudicial or derogatory terms to deseribe group
Exclusion: Advocating for removal, segregation, marginalization of group the meme does not use any these attack types, respond with Benign.
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Does this meme express hate or negative bias toward any of the following protected groups?
Religion: A group defined by shared belicf systems
Race: A group defined by racialized physical characteristics
Sex: A group defined by sexual attributes or sexual identification
Nationality: A group defined by country or region of origin
Disability: group defined by conditions leading permanent dependencies
the meme does not target any protected group, respond with Benign.
MAMI. For policy-manual SFT, we use the following prompts:
Based on the content and context of this meme, does use any of the following types of attack against
group?
Choose from the following options:
objectification: The content reduces individuals groups objects, ignoring their personhood or
agency
shaming: The content ridicules, mocks, or publicly humiliates individuals groups
stereotype: The content attributes oversimplified, generalized, or exaggerated traits individuals or groups
violence: The content depicts or encourages physical harm, threats, or violent actions against indi- viduals or groups
If the meme does not contain any hateful content, respond with Benign.
What the attack type?
PrideMM. For palicy-manual SFT, we use the following prompts:
Based on the content and context this meme, does this meme express hate or negative bias toward any of the following targets?
Choose from the following options:
undirected: General targeting without specific individuals or groups
individual: Targeting specific individuals
community: Targeting LGBTQ+ communities or groups
organization: Targeting specific organizations or institutions the meme does not contain any hateful content, respond with Benign.
What the target type?
Based on the content and context of this meme, what the stance towards LGBTQ+ individuals or communities?
Choose from the following options:
neutral: The content does not express clear support or opposition
support: The content expresses positive attitudes or support
oppose: The content expresses negative attitudes or opposition
What the stance?
B.3 DATASET STATISTICS
Binary Classification statistics. Table 4 shows the data split for our binary evaluation datasets. For HatefulMemes, we use the dev_seen split as the validation set, test_seen as test set.
Fine-grained Classification Statistics. Table reports the detailed distribution of fine-grained attributes in the HatefulMemes dataset, covering both attack types and protected categories. Note that we use the dev_unseen, split for final evaluation.
Table provides the fine-grained label distributions for the MAMI dataset, focusing on Sub-task (Type of Misogyny).
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Table 4: Statistical summary of binary classification datasets.
Datasets
Train
Test
#Benign
#Hate
#Benign
#Hate
HatefulMemes
5450
3050
500
500
MAMI
4500
4500
500
500
PrideMM
2581
2482
260
247
Table 5: Statistics of fine-grained attributes in the HatefulMemes dataset, showing attack types and protected categories across train, dev_unseen, and dev_seen splits.
Fine-grained types
train
dev jev_unseen
dev dev_seen
dehumanizing inferiority
1318
104
121
658
35
49
mnciiing-violence
407
Attack type
mocking
378
29
contempt
235
10
slurs
205
4
6
exclusion
114
13
religion
1078
77
95
1008
63
78
Protected category
sex
nationality
325
26
disability
255
17
22
Table summarizes the fine-grained label distributions for the PrideMM dataset, including both target categories and stance annotations across the training and test splits.
Hateful Reasoning Corpus Statistics. Table 8 presents the dataset statistics for Hatred, which only includes hateful memes paired with explanations. The test set corresponds the original Hateful Memes dev_seen split.
B.4 DATASET LICENSES
To access the Facebook HatefulMemes dataset, one must follow the license from Facebook HarMeme and Harm-P distributed for research purposes only, without a license for commer- cial use. MAMI is under Apache License 2.0. There no specified license for PrideMM.
C EXPERIMENT SETUP AND IMPLEMENTATION DETAILS
Software Environment. PyTorch 2.5.1. 1, CUDA 12.4. 4, Huggingface formsformer 4. 45.0 and Python 3.10.12 12 were used for implementing the experiments. All the reported metrics were computed by TorchMetrics 0.0.1.
Hardware Environment. We conducted our GRPO and EXPO-HM experiments on a server equipped with 8 Nvidia H100 with 80GB of VRAM. For the DPO and SFT baselines, GPU.
Training Details
We freeze the vision module throughout fine-tuning, following the standard LMM fine-tuning protocol. We conduct all training with LoRA (Hu al.) 2022). with LoRA rank=64, a = 128. For DPO sampling and all the inference, we use vLL.M inference engine 0. 9.
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Table 6: Statistics of Sub-task B in the MAMI dataset: type of misogyny labels across training and test sets. We treat this as multilabel, multiclass fine-grained classification task.
Category
Training Set
Test Set
Shaming
1274 (25,48%)
146 (29.20%)
Stereotype
2810 (56.20%)
350 (70.00%)
Objectification
2202 (44.04%)
348 (69.60%)
Violence
953 (19.06%)
153 (30.60%)
Table Statistics of fine-grained attributes in the PrideMM dataset, showing target categories and stance labels across training and test sets.
Fine-grained types
Train
Test
Benign
2208
260
Undirected
666
68
Individual
219
19
Target
Community
986
122
Organization
249
Neutral
1252
140
Stance
Support
1645
182
Opposc
1431
185
C.I SFT AND DPO TRAINING
employ the officially recommended fine-tuning library LiaMA-Factory 0. 9. f with official hyperparameter settings for all training tasks in both the SFT and DPO, except for the LoRA config that we mentioned above. For DPO, we sweep 3 = 0.1, 0.3, 0.5, 0.7, 0.9 and report the best results. For all runs, we train for 3 epochs, and then select the best checkpoint based on validation performance.
For Qwen2.5-VL. fine-tuning,
C.2 GRPO TRAINING
We use the VeRL library verl 0. 0.4.1 We use the default hyperparameter settings for all training except for the LoRA configuration. For all runs, train for 3 epochs, and then select the best checkpoint based on validation performance. The run time for ExPO-HM is about 4 hours on GPUs, which the for the baseline GRPO experiment.
C.3 CDE REWARD HYPERPARAMETER
For CDE hyperparameters, we conduct standard hyperparameter tuning via grid search on the Hate- fulMemes validation set. Once the optimal values were identified, we fixed these parameters and applied them directly to MAMI and PrideMM.
We observe that as long the hyperparameters fall within reasonable range, the model perfor- mance remains highly stable. Based on our hyperparameter tuning, we recommend the following 0.1, b 0.5, = 0.2, and p 0.25. The stable
default hyperparameters for new datasets: ranges, within which performance differences remain small, are: 0.05 < a S 0.15, 0.4 < < 0.6, 0.15 < 0.25, and 0.1 < p < 0.5.
The following observations summarize the sensitivity characteristics of each CDE hyperparameter:
The following intuitions summarize the observed sensitivity patterns:
Low-entropy cutoff (a): too small (e.g., < 0.05), the reward provides limited benefit and tends to encourage overconfident predictions, leading to larger KL divergence
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Table Statisties of the Hatred dataset, which only includes hateful memes with explanations. The test set corresponds to the original MatefulMemes dev-seen split.
train
test
total
#HHttred (hateful memes only)
2,982
246
3,228
and reduced policy entropy. a is too large (e.g.. 0.20), the CDE reward becomes too easy to satisfy, resulting in diminished improvement over the default value. A default value of a 0.1 consistently performs well.
High-entropy cutoff (b): A small value of b (e.g.. 0.25) excessively narrows the confidence band, pushing predictions into the overconfident region, significantly increasing KL diver- gence, reducing policy entropy, and destabilizing training. Conversely, overly large values make the reward too permissive and degrade performance. The recommended default value of 0.5 achieves balance between stability and effectiveness.
CDE weight (w): Large weights cause the CDE reward to dominate over the accuracy reward, which degrades performance when w >> 0.5 compared to default value of 0.2. Extremely small weights, on the other hand, yield only marginal improvements over the baseline. moderate value (w 0.2) is most effective.
Penalty rate (p): Hyperparameter tuning for P shows relatively low sensitivity. The default value works reliably across datasets. Performance degrades compared to the default setting when the penalty is removed (values near zero) when it becomes overly strong (e.g., p = 1.0).
D EVALUATION OF MODEL-GENERATED REASONING
Following prior work Yang et al. 2023: Mei et al. 2025 we assess explanation quality using an
LLM judge. Specifically, we provide GPT-4o mini (gpt
fo-mini- - 2024-07-22200 with refer-
ence explanations from (Hee et al., 2023). Following previous works (Mei et al. 2025), we adopt the same prompt:
Compare the model generatee reasoning with the reference human reasoning for this hateful meme.
Reference:
Model: (reference_reasone reasoning)
Model Prediction:
{model prediction
Rate how well the model reasoning aligns with the reference scale of 0-10:
99-10: Excellent alignment, captures all key points
7-88 Good alignment, captures
key points
- 5-6: Satisfactory alignment, captures some key points
3-4: Poor alignment, misses many key points
- 1-2: Very poor alignment, minimal understanding
Completely wrong or unrelated
Score: [0-10]
Explanation: [1-2 sentences]
E ADDITIONAL RESULTS ON MODEL- MODEL-GENERATED REASONING EVALUATION
In this section, we provide extended experiments and analysis for the Luudas-a evaluation used in work. While L.M--ased evaluators are widely adopted for assessing explanation qual-
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and reasoning consistency, concerns remain regarding reproducibility, prompt sensitivity, and potential model deprecation. We address these concerns through (1) evaluation across multiple open-source judges, (2) inclusion of prompt-free metrics, and (3) sensitivity analysis with para- phrased prompts.
ity
E.1 EVALUATION ACROSS MULTIPLE LLM JUDGES
To ensure a fair comparison, we adopt the same judge model and the same evaluation prompt prior work (Mei et al.] 2025) in the main text. However, we acknowledge that evaluation with a commercial closed-source model may raise reproducibil- ity concerns, particularly if the model deprecated in the future. To address this, we conduct additional evaluations using open-source LLM judges with the same prompt for reproducibility, including I-ntsuuutttt 2507, gpt-ost oss- 1ass-200 20b, and genma 1omum--3-4--a 3-4b-it. We further report scores using newer and stronger closed-source judge GPT-5 (gpt-5-2025-08-0 for reference.
We also include BERTScore Zhang et al. 2020). which measures sequence-level semantic simi- larity between human rationales and model-generated explanations. Because BERTScore does not rely on prompts, it serves as a fully reproducible complement to LLM-based evaluations.
We compute BERTScore using the official implementationf" Following the same setup as our LLM-- as-a-judge evaluation, nales and compute the BERTScore for each model-generated explanation.
the Hatred annotations from (Hee et al. 2023) the reference ratio-
Table Extended LLM-ee-a-judde and BERTScore evaluation across models and training methods.
Higher is better. The hest-performing training method for each model is shown in bold.
Model
GPT-4o mini
GPT-5
Qwen3
Gemma-3
gpt-oss
BERTScore
Qwen2.5-VL-33)
Zero-shot
3.3
1.5
2.0
2.9
2.4
0.52
SFT
3.6
2.0
2.4
3.8
2.6
0.53
DPO
3.5
2.2
2.3
3.7
3.0
0.53
GRPO
3.8
2.8
3.0
4.2
3.2
0.53
EXPO-HM
5.1
3.6
4,2
5.5
4.0
0,56
Qwen2.5-VIL-77
Zero-shot
5.0
3.8
4.5
5.2
4.0
0.55
SFT
5.0
4.0
4.6
5.5
3,9
0.55
DPO
4.9
3.5
4.5
4.9
3,6
0.54
GRPO
5.2
4.1
4.7
5.9
4.6
0.59
EXPO-HM
6.2
5.0
5.5
7.0
5.3
0.65
Table shows the detailed results.
We that different LLM judges exhibit different scoring distributions. BERTScore is less sitive to subtle performance differences, likely due to its limited semantic understanding compared modern LLMs. GPT-5. Qwen3, and gpt-oss tend to be more strict, while Gemma-3 tends to be more generous. Nevertheless, ExPO-HM models are consistently rated as the best-performing sys- tem under all evaluation metrics with substantial margins over GRPO models. This further validates the effectiveness of ExPO-HM.
E.2 PROMPT SENSITIVITY ANALYSIS
To study prompt robustness, we manually paraphrased the evaluation prompt and re-evaluated all models using gpt-4o -2024-07-1111 As shown in Table TO] the results remain largely unchanged, suggesting that our evaluation is not sensitive to prompt phrasing.
Below the paraphrased prompt for reference:
https://gittuub.com/tte
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Table 10: Prompt sensitivity analysis using GPT-4o mini the judge. Higher is better.
Modcl
Original Prompt
Paraphrased Prompt
Qwen2.5-VL-30
Zero-shot
3.3
3.2
SFT
3.6
3.4
DPO
3.5
3,3
GRPO
3.8
3.9
EXPO-HM
5.1
5.1
Qwen2.5- VL-7B
Zero-shot
5.0
5.0
SFT
5.0
5.2
DPO
4.9
5.0
GRPO
5.2
5.1
EXPO-HM
6.2
6.3
Evaluate how closely the model's explanation matches the human reference rationale for this hateful meme.
Reference: freference freference.reasoning
Model: (model reasoning) Model Prediction: (model prediction)
Rate how well the model reasoning aligns with the reference on a scale of 0-10:
99-10: Outstanding alignment, captures all major points
- 7-88: Strong alignment, captures most important points
-5-6: Moderate alignment, includes some relevant points
3-44 Weak alignment, overlooks many key points
- 1-:2: Very weak alignment, shows little understanding
0: Completely incorrect unrelated
If the model prediction hateful, which is incorrect, the highest score you may assign
is 2.
Explanation: [1-2 sentences]
Score: [0-10]
F HUMAN EVALUATION ON MODEL--GENERTER REASONING
To assess the quality of the reasoning beyond LLM-as-a LLM-as-a-judge, we further conduct two complemen- tary human evaluations. Each example is independently evaluated by three crowd-sourced annota- tors with at least an undergraduate degree and demonstrated familiarity with internet meme culture. We evaluate both the GRPO baseline and our ExPO-HM model, and we randomize the order in which model outputs are presented to mitigate ordering or anchoring biases. An illustration of the annotation interface is provided in Figure
COHERENCE EVALUATION
Annotators judge whether the model's final decision is logically supported by its rationale. For each example, we ask:
"Does the model's final decision logically follow from its rationale? In other words, the decision grounded in the explanation provided?"
Annotators answer Yes or No. The GRPO baseline achieves 96% coherent outputs, while ExPO- HM achieves 100% coherence.
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Example
Image 10: 85491
Model
Dechion:
Rationale: "tronry" derogatory offenshve
people. unquaçe dehumantting
Model
Deciion: YES
tranny for sale
The meme contain the test tran for sale." which is de cepttoyyy torn used rofor to tranagender language
stanectypan tronggnddr community. The use such language in mome generally cons corid hateful and direspecffll
Ground Annotation
community
Expert Reasoning:
dehumanizu the lobt commurity as oisects by sugaesting that transpenders can be put sale.
Your Evalustion
logically
Model
Model
Helpfulness helpdul rationale understanding
Model
Unwited
Holh Hokk
0
Optional: Additional Comments
Figure 4: Human evaluation interface. Annotators assess the coherence and helpfulness of model- generated rationales, with access to the meme and ground-truth expert annotation.
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F.2 HELPFULNESS EVALUATION
Annotators also rate how helpful the rationale is for understanding why the meme hateful. For each example, we ask the annotator:
"How helpful is the model's rationale for understanding why the meme is hateful benign?"
We adopt a 0-4 Likert scale (adapted from prior work (Wang et al. 2024b)), defined as:
0 Unrelated / Incorrect: Rationale is irrelevant, incorrect, or unusable; provides no moderation value.
Weakly Related: Touches on the content but lacks specificity or clarity; cannot sup- port moderation.
2 - Partially Helpful:
Contains some relevant entities or violation cues but is incomplete partially incorrect; usable only with major edits.
3 - Mostly Helpful: editing.
Identifies turget/violation with minor inaccuracies; usable with light
Highly Helpful: Fully accurate and specific; clearly identifies entities and violations; directly usable as moderation rationale.
-
Annotators additionally given the gold human rationale for reference, consistent with the LLM- as-a-judge setup.
We obtain average helpfulness scores of 1.6 for GRPO and 2.2 for EXPO-HM. After normalizing
the scores to the same 0-10 scale used by the LLM-as-aajudge we observe high agreement between human and LLM evaluations in the relative improvement from GRPO to ExPO-HM. We provide detailed comparison in Table [1 Inter-annotator agreement is strong, with Krippendorff's Cordinal 0.71.
Table 11: Comparison of human and LLM evaluation of explanation quality. Human scores are reported in both the original 0-4 Likert scale and normalized 0-10 scale for comparability with LLM-as-a-judge scores.
Model
LLM (GPT-4o mini)
LLM (GPT-5)
Human (0-4, Raw)
Human (0-10, Scaled)
GRPO
5.2
4.1
1.6
4.1
ExPO-HM
6.2
5.0
2.2
5.5
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G CDE DERIVATION AND ALTERNATIVE ESTIMATOR
G.1 CDE DERIVATION
Here, provide the full derivation for the CDE metrics estimation. We consider the CDE metrics for a model parameter with 9 over a dataset D.
(d e, x) = Ex envar (x), (e.x) |- log po e, x)]
By Monte Carlo over the dataset
Be (xc), d= [log pa e,
D
xED
By Monte Carlo sampling K times
pa(e; x)
Σ e log e,
Approximate po (e;
x) Rc K
K
~
po(d e, x) e, x)
(12)
K
XED
(dde:;))
For the entropy Hdd c, x), we by default compute it over the full decision vocabulary:
H(d er, x) = Σ po (d) e; x) log po(d (;,x))
(13)
dEv
where denotes the output vocabulary. For practical efficiency, we do not compute entropy over the entire vocabulary; instead, we approximate using the top 10-50 tokens by likelihood, which substantially reduces computation and memory costs. When a fine-grained class is represented by multiple tokens, we compute the average token entropy similar the policy entropy computation.
For binary classification, one may collapse the vocabulary into Yes, No by grouping all tokens semantically aligned with "yes/positive" "no/negative." and normalizing their probabilities.
G.2 ALTERNATIVE ESTIMATOR THROOGHI CIAINN RULE
When considering the CDE, we can expand through: By the chain rule of entropy:
H((e, d) xx) = H(e x)
Hld e,
(14)
(1)
(2)
(3)
1. H((e, d) xx Sequence entropy: the total entropy of generating both reasoning and deci- sion.
2. H(e x) Reasoning entropy: measures the diversity of reasoning paths the model can produce for an input.
3. (d e, x. Conditional decision entropy (CDE): quantifies the uncertainty of the model's
decision given its own on-policy reasoning path
Both (1) and (2) can be estimated directly via sequence-level sampling. Then CDE, (3) can be obtained by subtraction, using the chain rule in Eq. [4]
G.3 ALTERNATIVE ESTIMATOR WHEN LOGITS NOT AVAILABLE
When model logits are not accessible, we approximate entropy by sampling K = 16 responses directly from the LMM and measuring entropy over the final detection decisions.
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H(d x) =
Σ dEXes,Nc
pa(d x), log (d x),
(15)
where po(d x) is estimated by counting the relative frequencies of positive vs. negative decisions among the K sampled responses. This can similarly be applied towards fine-grained classes.
To approximate full CDE without logits, one can fix reasoning trajectory and resample K responses conditioned on that reasoning (e.g., by sampling with temperature 0.7-1.0). The Monte Carlo estimator in Eq. [2 is then applied to obtain the CDE for each sampled reasoning path. Finally, averaging across multiple such sampled reasonings provides an overall CDE estimate.
H CDE FOR CONFIDENCE CALIBRATION
Maximizing the CDE reward implicitly encourages better confidence calibration. By design, the CDE reward penalizes confident wrong predictions and rewards confident correct decisions. This encourages well-behaved decision distribution: the model becomes confident only when it is likely to be correct, and becomes uncertain when the outcome is ambiguous.
H.1 CALIBRATION EVALUATION
To validate whether CDE reward improves calibration, we compute Expected Calibration Error (ECE) and Brier score using the model''s probability assigned to the final answer token, conditioned on the generated explanation under the Explain-then-Deee teein-ttte---etetttt setup. For ECE, we use 10 bins.
We evaluate calibration for zero-shot, SFT, GRPO, and ExPO-HM across both 3B and 7B model sizes. As shown in Table 12, ExPO-HM consistently achieves substantially better calibration than all baselines by large margin. Notably, for the 3B model, ExPO-HM reduces the Brier score from 0.590 + 0.283, indicating significantly improved decision reliability.
Table 12: Calibration metrics (ECE and Brier score) under the Explain-then-Detec setting, Lower values indicate better calibration. ExPO-HM achieves the best calibration across all configurations.
Model
Variant
ECE
Brier |
Zero-shot
0.525
0.590
SFT
0.486
0.534
Qwen2.5-VIL-3B
GRPO
0.394
0.441
ExPO-HM
0.232
0.283
Zero-shot
0.301
0.335
SFT
0,234
0.282
Qwen2.55VLL77B
GRPO
0.221
0.287
EXPO-HM
0.160
0.214
H.2 THEORETICAL ANALYSIS
We show that, under the ideal EXPO-HM policy, i.c., when the CDE reward is maximized, the induced decision distribution admits a Brier score that is upper bounded by small constant deter- mined only by the CDE entropy thresholds a and b. This provides theoretical justification for why optimizing CDE improves calibration.
Setup. Following the notation introduced in Sec. 3.1 let d {0, 1] denote the ground-truth label. Under our Explain-then-Deted formulation, the model produces a probability
p = (e = x;, e:).
(16)
To simplify the derivation, we instead let
Pi n (d) x,, ei)e
(17)
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which denotes the model probability assigned to the correct class.
The binary Brier score for sample can then be expressed as
BS: = (p. 1)²,
(18)
For convenience, we define correctness by
1, pi 0.5,
4
(19)
0, Pi < 0.5.
We compute the binary entropy via
h; H2(p;) -P, log - (1 -1-Pi log(1 ps).
(20)
where H2 denotes binary entropy in bits.
Entropy thresholds imposed by CDE. The CDE reward uses entropy thresholds: entropy cutoff a = 0.1 and High-entropy cutoff b = 0.5, defining:
Low-
h4 S a (high confidence).
h, 2 (low confidence).
(21)
Let the corresponding confidence thresholds be Po H2 (a), (a),
pb H2 H2 (b).
(22)
For brevity, we write H51 for the inverse of H2 on the relevant monotone branch (either [0, 1 1/2] or [1/2, 1)), depending whether are in the low- high-confidence regime.
Optimal ExPO-HM policy under CDE. Maximizing CDE encourages:
high confidence (h; a) for correct predictions (t; 1); high uncertainty (h; 2 for incorrect predictions (t, = 0).
The optimal CDE-maximizing decision distribution satisfies
4; = S p 1.0 and Pt 2 Pas
(23)
t; = 0 K P 0.5 and 2 Ps-
(24)
Thus, obtain
[Pa: 1].
if (correct prediction),
Pi E
(25)
[Pb, 0,5),
if = 0 (incorrect prediction).
is easy to find Pa 0.987, Pb 0.110 from Eq. 22
Bounding the Brier score. We bound the Brier score by considering the correct and incorrect predictions separately using the probability constraints in Eq. 25
Case 1: Correct predictions. When 1.
BS; = (p: - 1)2 S (pa - 1)2.
(26)
Case 2: Incorrect predictions. When t; = 0, similarly
BS, = (ps - 1)2 & (po - 1
(27)
Thus, for all samples,
BS, < (1 - (1-pa)² 4 1, (1 - p2) t,
(28)
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Table 13: Fine-grand Fine-grained attack types results on HatefulMemes. We report overall attack accuracy, micro Fl, and per-class FI for each attack type. Due to space constraints, we use abbreviated labels for attack types, the full names are provided in 5
Overall Metrics (%)
Attack Types F1 (%)
Model
Acc.
Micro Fl
Benign
Dehum.
Infer.
Mock.
Incit.
Excl.
Contempt
Slurs
Zero-shot
44.6
44.7
72.3
36.6
14.0
16.4
19.1
37.5
5.4
16.0
SFT 57.8
58.4
78.7
43.6
5.6
12.2
18.6
22.2
9.5
22.2
DPO
63.2
63.2
81.2
44.3
22.6
26.1
20.0
19.5
6.8
21.1
GRPO
79.6
36.5
12.0
18.4
23.4
21.7
EXPO-HM
74.8
75.6
84.6
62.1
46.0
60.0
59.5
61.2
53.3
66.7
Table 14: Fine- Line-grained Protected categories results on HatefulMemes. micro Fl, and per-class F1 for each protected category.
We report overall accuracy,
Overall Metrics (%)
Protected Categorics Fl (%)
Model
Micro Fl
Benign
Nationality
Religion
Race
Sex
Disability
Zero-shot
64.3
64.5
77.7
26.7
29.2
42.5
15.4
23.5
SFT
67.6
69.4
79.9
33.4
34.4
44.7
22.6
40.0
DPO
66.1
66.6
79.1
21.4
36.6
40.8
15.4
26.7
GRPO
64.1
64.5
75.5
23.1
64.0
49.1
54.9
35.7
EXPO-HM
76.5
77.2
84.7
61.1
67.1
51.5
58.8
51.9
Dataset-level bound.
Let Neorr and Nwwong be the number of correct and incorrect predictions in a dataset of size N. We have
Neorr
Nwrong (1 -pb)²
BS
BS:
<
N (1 - (1-Pa)2
(29)
N
E11
In the extreme (and likely unrealistic) worst where the model makes errors on all examples (Nwrong = N), the bound simplifies to
BS < (1 - pb) (1-p)) ~ 0.79.
(30)
For balanced binary classification scenario with 50% accuracy (Neorr Nwrong N/2). the bound becomes
Pa 2 (1 pb)² RJ 0.40.
BS <
(31)
2
These results show that, under the ideal EXPO-HM policy induced by maximizing the CDE reward, the decision distribution is constrained region with substantially lower Brier loss compared unconstrained policy. This provides theoretical support that ExPO-HM encourages more calibrated probability estimates.
I FINE-GRAINED RESULTS
In addition to the overall micro F1 reported the main text, we provide detailed fine- tne-grained results on the HatefulMemes dataset for both attack types and protected categories using the Qwen2.5-VV LL- 7B models in Table 13 and Table 14 For both fine-grained setups, we observe that the benign class achieves relatively high performance, whereas most fine-grained categories remain highly challeng- ing for existing baselines: many per-class scores fall below 50%. Recognizing different attack types is particularly difficult for all baselines, and certain protected categories, such tionality, are especially difficult. Overall, ExPO-HM shows substantial improvements across all fine-grained categories, demonstrating strong gains over all baseline systems.
and na-
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J COMPARING GRPO AND EXPO-HM PREDICTIONS
J.1. CASE ANALYSIS
Table 15 and Table 16 show examples where ExPO-HM corrects prediction made by the
GRPO baseline on Qwen2.5-VL.77. illustrating its ability to generate higher-quality explanations and leading correct final prediction.
Table 15: Part of the Visualization cases from GRPO Qwen2.5-VL-7B and ExPO-HM Qwen2.5VL-7B Models.
Case
Case 2
Case 3
Ims samdwach mokes douthes
a grouu oimen
the hariest part to eat
Meme
degetable
ironing hoard
Ground Truth GRPO Prediction ExPO-HM Prediction
Hateful
#Hateful #Benign
#Hateful
#Benign #Hateful
#Benign
#Hateful
#Hateful
The meme uses wheelchair as sual metaphor for the "hardest part to eat in vegetable." This is a play on words and is not intended to be offensive. The joke is common
The meme uses visual pun by showing dogs and labeling them "a group of men." This a common internet meme format that uses animals represent group of people, often humor- ous effect. The image itself does not contain any explicit content language that could be considered hateful. The use of animals to repre- sent group of people is common meme trope and generally not in- tended to be offensive.
The meme humor- ous observation about a sandwich maker that can be used as iron- ing board. It does contain any offensive or derogatory content di- rected any specific group or individual. The joke is based on a prac- tical use of a kitchen ap- pliance and does not ex- press any form of hate discrimination.
based phrase and the visual of a wheelchair, which tool for mobility, not a derogatory symbol. The meme is not targeting any specific group or race, and the humor is based on general observation rather than any harmful stereotype.
GRPO Explanation
meme uses a derogatory term to refer to group of men, which form of de- humanizing and be considered degrading. The use of dog to represent a group of is also example speciesism, which is form of prejudice against non-human animals. While the is explicitly violent, does contain elements that can he seen as dehumanizing and degrading
The derogatory term "veg- etable" to refer to a person in wheelchair, which is form of and
The meme makes a derogatory comparison by using woman's body represent an ironing board, which is dchumanizing and objectifying portrayal. This type of humor often used to degrade women and is consid- ered dehumanizing.
dehumanizing derogatory language. This type of language is considered hateful it dehumanizes and degrades the person the wheelchair
EXPO-HM Explanation
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J.2 ERROR ANALYSIS
During the analysis of the common error cases shared by both the GRPO baseline and EXPO-HM, Le., cases that EXPO-HM is still unable correct, observe major categories of failures. The first category consists of highly implicit memes, whose harmful intent can only be uncovered through complex, multi-step reasoning. The second category includes memes that require exter- nal world knowledge and/or subtle contextual linkage. making their interpretation dependent on background information not explicitly contained in the meme itself. These examples are inherently difficult, and even human annotators may misinterpret them Kiela et al. (2020). Table 17 presents three representative cases.
The first meme uses apparently nonsensical statement ("potato is my favorite color of the alpha- bet") to implicitly portray the individual as extremely unintelligent. The models fail to (i) infer the underlying stereotype associated with intellectual disability, and (ii) connect the negative depiction of cognitive ability to the corresponding protected group defined the policy manual.
The second meme requires linking the reference to 9/11 with broader immigration discourse, yet the models fail to associate the event with immigrant sentiment or identify the targeted group.
For the third meme, the models' rationales focus narrowly the absence of explicit slurs group identifiers and interpret "trash" as generic political metaphor, thereby overlooking its dehumaniz- ing toward the group depicted in the image.
For future work, developing a larger corpus of implicit hate-speech examples with high-quality chain-of-thought annotations may help pretrain models to better understand subtle and context- dependent hateful content that requires complex reasoning. In addition, incorporating retrieval aug- mented generation pipeline to supply relevant external knowledge, such historical, geopolitical, or stereotype-related context, may further improve the model's ability to identify implicit hate that directly observable from the meme alone.
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Table 16: Part 2 of the Visualization cases from GRPO Qwen2.5-VL-77 and ExPO-HM Qwen2.5VL-7B Models.
Case
Case
Case
Meme
Ground Truth GRPO Prediction ExPO-HM Prediction
#Hateful #Benign #Hateful
#Hateful #Benign #Hateful
#Hateful
#Benign
#Hateful
The meme appears a humorous take on a political situation, us- ing exaggerated expres- sions and juxtaposi- images convey a message. The top im- age shows person with a hearded face, possi- bly representing polit- ical figure. The hottom image shows person aiming shotgun, which could be interpreted a metaphor for taking back what was claimed. The use of such imagery and text is not inherently hateful but rather form political commentary or satire.
The meme depicts person covered soap suds, which com- mon reaction to too much soap in dish- washer. The text hu- morously suggests that someone has added too much soap to the dish- washer again. There elements in the im- age or text that indicate form of discrimi- nation hate towards specific group. The meme a light-hearted joke about a common household issue.
The meme makes claim about the exis- tence Muhammad based on a scientific
discovery. which is religious and historical topic. However, the meme does not use any offensive language or imagery that would be considered hate speech. humorous and speculative statement, not direct attack on any group or individual.
GRPO Explanation
The meme makes false and absurd claim about the existence Muhammad based on fictional scenario involving human DNA not goat carcass. This scientific dis- covery and is intended to be humorous at the expense of religious beliefs. The content not respectful to any religious group and can be seen as mocking degrading.
The meme contains a provocative statement and imagery that could interpreted as threat- ening or aggressive towards muslims. The of a religious figure and the phrase "we're 'e'r taking back" suggests a sense of reclaiming or asserting dominance, which be seen form of hate speech.
The meme uses derogatory term "dish- washer" way that could be seen as dehu- manizing, as it reduces person to machine. This type of language can considered dehu- manizing and is often used in a derogatory manner.
EXPO-HM Explanation
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speech language towards specific group. The content is a political commentary using metaphor about trash and a garbage collector, which is common way Published as a conference paper at ICLR 2026
Table 17: Visualization of error cases from GRPO Qwen2.5-VL-77 and ExPO-HM Qwen2.5VL-7B Models.
Error Case
Error Case 2
Error Case 3
to hyorrii
Meme
Oiflie albbalee
Ground Truth GRPO Prediction ExPO-HM Prediction
#aaefful #Benign #Benign
#ataefu
#Haatful #Benign #Benign
#Benign
#Benign
The meme references tragic event and makes statement about the safety of allowing ev- eryone into the US. It does not use any of- fensive language agery would con- sidered hateful towards a specific group. The statement is general observation about the balance between safety and freedom of entry, which is topic of public debate and con- cern. The meme does not target any particular group or individual in derogatory manner.
The meme does not contain explicit hate derogatory
The meme uses a play on words where "potato" is pun the letter "P" in the alpha- bet. a humorous and light-hearted joke that does not contain any offensive harmful content.
im-
GRPO Explanation
to express dissatisfac- tion with group situation. The meme does not target any particular race, religion, nationality. or other pro- tected characteristics.
The meme does not con- lain any explicit hate speech or slurs. is a factual statement about the 9/11 attacks and the subsequent se- curity measures. The language used neu- tral and does not express any bias prejudice against any group. The meme is not intended to incite hatred or discrim- ination.
The meme appears to be political statement, likely referencing the tire of the individuals in the image, which all white. The meme does not contain any explicit implicit hate speech directed at any specific group or individual. The context and intent of the be politi- cal rather than hateful.
The meme does not con- lain any explicit or plicit content that is con- sidered hateful. It is humorous statement about person's prefer- ence for the letter 'P' in the alphabet, which is common internet meme.
EXPO-HM Explanation
meme
34


Reviews:
[{"review_id": "r8ZlRIcMl7", "reviewer": "Reviewer_ttL2", "summary": "This study propose a method, which is referred to as Explain-then-Detect Policy Optimization, for hateful meme classification and explanation generation, which is mainly utilising the idea of how manual annotation processes are conducted. It demonstrate that combining SFT-warmup, GRPO with curriculum learning and conditional decision entropy improves the performance for both detection and reasoning.", "questions": "- Is it possible to provide details fine-grained results for attack types and targets? \n- How exactly were the policy manuals built from the annotation guideline? For examples there is not detailed annotation guideline for hateful memes.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Focused on the limitation of current CoT based approaches, which underperforms SFT based model. \n- The proposed approach inspired by how \"human annotators are trained for the annotation\" is well motivated. \n- Experimental results on different meme datasets to show the effectiveness of the proposed approach.\n- Task addressed - binary, fine-grained and reasoning\n- Ablation studies highlights the importances of the different components of the proposed  approach.\n- To ensure reproducibility authors plans to release augmented data and code upon publication.", "weaknesses": "- While LLM-as-a judge for explanation/reasoning evaluation is becoming widely used approach, however, they are limited in many setups. Related to this, prompt plays a role for judge and reproducibility if often challenging. \n-  Hyperparameters (e.g., CDE) can be sensitive to the dataset, how are they set for different datasets?\n\nTypos/Grammatical issues\n- L130: Specify what CXHXW refers to\n- L187: scarce taionale corpora\n- 213: \"study policy guidelines\" -> study annotation guidelines?\n- 236: \"reward In\" please check, there might be fullstop or colon.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "KNBDvWYmpk", "reviewer": "Reviewer_oNni", "summary": "This paper introduces EXPO-HM, a method for fine-tuning multimodal local LMs (LMMs) to classify meme images as hateful or not in an “explain-then-predict“ manner, producing both an explanation and a prediction. The method involves two training stages: (1) a warmup SFT stage (SFT-PM) which tunes the LMM to predict the fine-grained image class using a set moderation guideline as additional; (2) a GRPO with curriculum learning (GRPO-CL) stage using a reward function that punishes (A) departure from an explain-then-predict output format and (B) inaccuracy with respect to the ground truth label. The “curriculum“ aspect pertains to the label used--the GRPO-CL process starts out using fine-grained ground truth labels before transitioning to a mix of fine-grained and binary labels. Finally, a variant of GRPO-CL is explored using conditional decision entropy (CDE) CDE as an additional reward signal, which measures the entropy of the label decision conditioned on the produced explanation.\n\nThe proposed method is compared to a number of baselines across three hateful meme datasets, including non-explain-then-predict baselines, previously published models, and explain-then-predict models using SFT, DPO, and standard GRPO. EXPO-HM is found to consistently outperform all baselines and prior models on all three datasets. API based models are not compared due to a high rate of rejection of the hateful meme inputs. An ablation study is performed to assess the marginal impact of each part of the training (SFT-PM, GRPO-CL, and the CDE reward component), which finds that all three provide marginal utility. An additional analysis finds that SFT-PM the most effective of a number of possible SFT warmup stages.", "questions": "No particular questions", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Important task:** The paper covers an important and not particularly solved task, that of hateful meme detection.\n    \n- **Comprehensive baselines:** The paper uses a very comprehensive set of baselines, including both prior work, direct detection baselines, and explain-then-detect baselines using a variety of levels of training including SFT alone, DPO, and standard GRPO. The paper provides a reasonable justification for not including API-based models such as the OpenAI o-series. I can’t think of anything that is missing.\n    \n- **Good results:** The proposed method outperforms all baselines and prior models. The improvement is a little incremental over the previous state of the art (+~1% F1), but it is consistent across all three datasets and it uses a very different method (no retrieval augmentation).\n    \n- **Good ablation studies:** The ablation studies do a nice job of validating the individual pieces of the EXPO-HM method, which might otherwise seem a little arbitrary and over-engineered.", "weaknesses": "**Minor** **spelling mistakes:** “taionale”, “subjectuive“ line186", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "srdSWvhOks", "reviewer": "Reviewer_nfaS", "summary": "The paper proposes ExPO-HM, an explain-then-detect framework for hateful meme detection. It addresses two challenges in prior explain-then-detect systems: missing policy-relevant cues in explanations and weak binary rewards that do not guide reasoning. Among three datasets (HatefulMemes, MAMI, PrideMM), ExPO-HM reports state-of-the-art performance on classification.", "questions": "Do you have any human evaluations of rationale faithfulness beyond LLM-as-judge?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The change from direct detection to explain-then-detect in hateful memes is valuable for real-world moderation: explanations provide context, policy linkage, and operational guidance.\n2. Extensive experiments are conducted on three public datasets to demonstrate its performance.", "weaknesses": "1. The proposed method shows heavy reliance on LLM-as-judge for rationale quality. It's reasonable to introduce different LLMs to evaluate the effectiveness of the proposed three components. Do we need to make new policies for different platforms?\n2. Failure cases are not reported. \n3. The CDE reward depends on the model’s own decision distribution; while penalizing confident errors helps, more experiments on calibration and robustness would strengthen confidence.\n4. Limited discussion on explanation faithfulness. Does the rationale actually reflect features used for the decision?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)