Consis-GCPO: Consistency-Preserving Group Causal Preference Optimization for  Vision Customization
Authors: Qiaoqiao Jin
Dong She
Siming Fu
Hualiang Wang
Mu Liu
Jidong Jiang
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-Subject Personalized Generation
Diffusion Model
Reinforcement Learning
Primary area: generative models

Abstract: Subject-driven generation faces a fundamental challenge: achieving high subject fidelity while maintaining semantic alignment with textual descriptions. While recent GRPO-based approaches have shown promise in aligning generative models with human preferences, they apply uniform optimization across all denoising timesteps, ignoring the temporal dynamics of how textual and visual conditions influence generation. We present Consis-GCPO, a causal reinforcement learning framework that reformulates multi-modal condition generation through discrete-time causal modeling. Our key insight is that different conditioning signals exert varying influence throughout the denoising process—text guides semantic structure in early steps while visual references anchor details in later stages. By introducing decoupled causal intervention trajectories, we quantify instantaneous causal effects at each timestep, transforming these measurements into temporally-weighted advantages for targeted optimization. This approach enables precise tracking of textual and visual contributions, ensuring accurate credit assignment for each conditioning modality. Extensive experiments demonstrate that Consis-GCPO significantly advances personalized generation, achieving superior subject consistency while preserving strong text-following capabilities, particularly excelling in complex multi-subject scenarios.

Published as a conference paper at ICLR 2026
CONSIS-GCPO: CONSISTENCY-PRESSEVIING GROUP CAUSAL PREFERENCE OPTIMIZATION FOR VISION
CUSTOMIZATION
Qiaoqiao Jin'; Dong She Siming Fu Hualiang Wang", Jidong Jiang
Shanghai Jiao Tong University University of Science and Technology of China
3 ZZejiang University "The Hong Kong University of Science and Technology Nanjing University
{jingiaooiaa]@esjttuuc
Figure Consis-GCPO achieves high-quality personalized generation across diverse scenarios. Top: R2I generation with complex multi-subject compositions and interactions. Bottom: R2V gen- eration demonstrating temporal consistency and subject fidelity in motion sequences. Corresponding prompts and additional visualization results are provided in the Appendix.
ABSTRACT
Subject-driven generation faces fundamental challenge: achieving high subject fidelity while maintaining semantic alignment with textual descriptions. While recent GRPO-based approaches have shown promise aligning generative mod- els with human preferences, they apply uniform optimization across all denoising timesteps, ignoring the temporal dynamics of how textual and visual conditions influence generation. We present Consis-GCPO, a causal reinforcement learning framework that reformulates multi-moda condition generation through discrete- time causal modeling. Our key insight is that different conditioning signals exert varying influence throughout the denoising process process-text guides semantic struc- ture in early steps while visual references anchor details in later stages. By in- troducing decoupled causal intervention trajectories, we quantify instantaneous
"quual contribution.
CCorespooniig author.

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Published as a conference paper at ICLR 2026
causal effects at each timestep, transforming these measurements into temporally- weighted advantages for targeted optimization. This approach enables precise tracking of textual and visual contributions, ensuring accurate credit assignment for each conditioning modality. Extensive experiments demonstrate that Consis- GCPO significantly advances personalized generation, achieving superior subject consistency while preserving strong lext-following capabilities, particularly ex- celling in complex multi-subject scenarios.
INTRODUCTION
Personalized content creation (Ruiz ct al. 2023: Wu ct al. 2025 Mou et al., 2025| Chen al. 2025
She et al., 2025] Jiang et al. 2024) has emerged as a critical capability in generative modeling, enabling users to synthesize diverse outputs that maintain consistency with provided references. In the image domain, reference-to-image (R21) generation has achieved remarkable progress through various adaptation strategies, from full-model fine-tuning and parameter-efficient techniques like LoRA (Hu et al.) to lightweight conditioning mechanisms using subject embeddings. Recent R21 methods such as DreamO (Mou et al. 2025), XVerse (Chen et al., 2025), and MOSAIC [She et al. (2025) have further advanced the field by introducing multi-reference subject generation, enabling consistent synthesis across multiple input references. Building upon these advances, reference- to-video (R2V) generation extends personalization to the temporal domain, with frameworks like VACE Jiang et al. (2025) and Phantom [iu et al. (2025c) demonstrating compelling results for both single- and multi-subject video generation while preserving identity consistency across frames.
Despite these technical achievements, current approaches face fundamental limitations balanc- ing competing objectives that prevent them from meeting human preferences as shown in Figure Subject fidelity degradation manifests when models struggle to preserve fine-grained characteris- ties and identity consistency, particularly in complex multi-subject compositions where interactions between subjects must be maintained. Concurrently, semantic alignment drift emerges as visual reference conditioning often interferes with the model's text-following capabilities, causing gener- ated content to prioritize visual similarity at the expense of semantic coherence. These dual chal- lenges result in problematic trade-off: models either produce visually accurate outputs that ignore textual instructions or semantically correct generations that fail to maintain subject identity. This in- ability to simultaneously satisfy both visual consistency and semantic alignment creates significant barriers to real-world deployment.
Recent advances in reinforcement learning for generation, particularly Group Relative Policy Opti- mization (GRPO) methodologies like Flow-GRPO (Liu et al. 2025a) and DanceGRPO (Xue al.) 2025), have demonstrated success in aligning models with human preferences, offering a promising avenue to address these challenges. However, these approaches suffer from critical shortcomings when applied to multimodal generation: temporal blindness-tie ignore how the importance of textual versus visual conditioning varies across denoising timesteps, applying uniform optimization throughout the generation trajectory; and entangled feedback they provide only terminal rewards without decomposing the individual contributions of text and reference conditions, making targeted improvements impossible.
To address these fundamental limitations, we propose Consis-GCPO, a principled framework that reformulates multi-condition guided generation as temporal causal optimization problem. The discrete-time structural causal model (SCM) that explicitly mod- els causal dependencies between conditioning signals and generation outcomes at each denoising timestep-addressingg addressing the critical gap in existing methods' inability to capture temporal dynamics of multimodal conditions. Leveraging this causal foundation, Consis-GCPo implements decoupled causal interventions through targeted ablations that selectively remove specific conditions individ- ual timesteps. By comparing main generation trajectories with prompt and reference-interventie paths, we precisely quantify when textual semantics versus visual references are most critical during denoising. These instantaneous causal effects are converted into temporally-weighte advantages that enable dynamic adjustment of semantic alignment and subject fidelity based on each timestep's 's actual importance rather than uniform treatment. The resulting optimization adaptively integrates textual and visual guidance based on measured temporal contributions rather than heuristic weight- ing schemes. Notably, method achieves superior multimodal consistency while maintaining
foundation of Consis-GCPO
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Prompt
Flow-GRPO Consis-GCPO
Reference
Flow-GRPO
Consis-GCPO
oute bear phohie with white wings gracefully acoring through the bright
sky, surrounded by fuiffy clouds glowing in the surlight.
A man sits by the window of
Prompt
hand a
toward outside. Sunlight reflects
relined gray tabby
cal, perched regally
gioss, with blurred acenery
the of polished
background...
lable, lifts ihs
dender
elegont poise and gaally gleaming
beby boy alts weeden crib, smiling brighlly he
against ils
adjustngg sunglasses.
stretches hard loward
colorful mobile hanging
graceful motton faint
above, while his other
cancleiiht Fickers
hugs heddy
arress scene.
Figure 2: Challenges in subject-driven generation. Current approaches struggle to simultaneously achieve subject fidelity and semantic alignment in R2I (left) and R2V (right). Our Consis-GCPP addresses these limitations through temporally-aware causal interventions that optimize condition- ing effects across different denoising timesteps.
computational efficiency, particularly excelling in complex multi-subject scenarios that current ap- proaches struggle with. Our contributions are as follows:
We identify the fundamental limitation of existing GRPO methods in subject-driven gen- eration- the inability to capture temporal dynamics of conditioning signals. Our proposed Consis-GCPO addresses this through causal reformulation and temporal intervention to estimate causal effect of visual and textual conditions, enabling precise quantification of when textual semantics versus visual references most critical during denoising.
We introduce decoupled causal intervention and lemporally-weighte advantage com- putation mechanisms that transform instantaneous causal effects into targeted optimization signals. This enables the model to dynamically adjust semantic alignment and subject fi- delity based on each timestep's actual importance rather than uniform treatment.
state-of-the of-the-art personalized generation methods, achieving superior subject consistency while preserving text-following capabili- ties. Our approach particularly excels in complex scenarios involving multiple subjects and intricate interactions, validating the effectiveness of causal-guided optimization.
We demonstrate significant improvements
2 RELATED WORKS
Subject-Drivee Generation
Subject-driven generation aims to synthesize visual content that pre- serves reference subject identity while following textual deseriptions. Early works like Dream- Booth (Ruiz et al., 2023). IP-Adapter (Ye et al.] 2023) and MS-Diffusion (Wang et al. 2025) achieved subject consistency through fine-tuning or attention mechanisms in UNet architectures, while recent transformer-basee
approaches including UNO (Wu et al. 2025) and X'Verse (Chen) al. 2025) leverage in-context learning for enhanced subject preservation. Video generation intro- duces additional temporal consistency challenges. MAGREF (Deng et al. 2025) addresses multi- subject video synthesis through masked guidance mechanisms, Phantom proposes unified text-image injection for cross-modal alignment, and VACE (Jiang et (025) introduces an all-in-one frame- work unifying generation and editing tasks. Despite these advances, existing methods struggle with balancing textual adherence and visual consistency, particularly in multi-subject scenarios where maintaining individual subject fidelity across temporal sequences remains challenging.
Reinforcement learning for image generation Reinforcement learning has recently emerged as an effective paradigm for enhancing text-to-image generation models. Flow-GRPÓ (Liu et al.] 2025a) first integrated online RI. into flow matching models through ODE-to-SDE conversion,
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demonstrating substantial improvements in compositional generation and visual text rendering tasks. DanceGRPO (Xue et al., 2025) adapts GRPO for visual generation, achieving consistent and stable policy optimization across diffusion models and rectified flows while scaling effectively to large and diverse prompt sets. These works collectively demonstrate that RI-baased optimization effec- tively addresses fundamental challenges in text-to-image generation, particularly in improving text adherence, compositional understanding, and human preference alignment while maintaining com- putational efficiency and preventing reward hacking behaviors.
PRELIMINARY: FLOW-GRPO
The starting point of our approach is the training of conditional flow model for reference-to-viitee generation (image or video). The typical input unit contains three components: (1) the textual prompt P. which specifies the human instruction; (2) the reference images I,, which inject the intended visual identity or style; and (3) the noised latent representation, defined as:
at (1 1) + lmi; I. E [0, 1],
(1)
where an denotes the target image and at is random noise sample. The conditional flow model v.(.) is then defined and trained to predict the velocity field that transports E towards Tp-
SDE-based iterative denoising Once trained, the conditional flow model can be applied in the generative stage by iteratively denoising random initialization. To enable sampling diversity and facilitate richer exploration during generation, we adopt Stochastic Differential Equation (SDE)
formulation:
Tt-A: P, L,, - At vs(a,, P, g(t)
(2)
where e, / (0, I) an i.i.d. Gaussian random variable introduced each denoising step, and g(t) is a time-dependent diffusion coefficient. Equivalently, the transition distribution can be written as
Tt-At N x2 -At vo P, ((t))tt
This stochastic formulation augments the deterministic ODE trajectory with Gaussian perturbations, thereby encouraging sampling diversity while still preserving conditional guidance.
On top of SDE-based denoising, Flow-GRPO (Liu al. 2025a) enhances flow-based generative
models through online reinforcement learning. The method treats the denoising process
quential decision problem, where the policy is defined as (t) po ax.) in Eqn Formally, the Flow-GRPO loss is defined as:
t=At
Co
Σ g=1
AL
- o, +)) - 3Dx
(4)
(= (mg)
where r0)
and At is the interval of inter time steps and clip is clamp function
Paas [
to restrict the value to the range [1 a. +
DISCRETE-TIME CAUSAL MODELING FOR MULTI-CONDITION GUIDED GENERATION
We reformulate the vision customization generation task through the lens of causal inference, en- abling principled analysis of how textual and visual conditioning jointly influence the generation process. Specifically, at each discrete timestep t in the reverse diffusion process, denoised state Ø;-Ar as causally determined by four parent variables: the current noisy latent w,, the textual prompt P, the reference image I,, and an independent noise term Ga. This relationship defines our structural causal model (SCM) for single-step denoising, i.e., (Iz, P,I,) - At-At-
model the
To quantify individual modal contributions, employ causal interventions through targeted ab- lations. Unlike global ablation methods, we introduce step-wise causal interventions that isolate causal effects at specific timesteps.
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Noisy Latent X
K Sample
X Sample
x K Sample
Step.
Xp-
SDE (P
SDE denoise
(e) SDE denoise
R
Continued SDE
Continued SDC
Ry
Step T-1
X
Xe-St
X
SDE
SDE denoise
SDE
R
Continued SDB
Continued SDe
(c) SDE (1, 8)
Decoupled intervention
Step
Reference temporal importance weights
R1
SDE (P 6)
SDE denoise
SDE D)
whs Softmoxi
Text temporal importance weights
Text-related reward
Reference-related reward
wa (f) Softmax(
(a) The sampling workflow of our Consis-GGPO
(4) Temporal importance re-weighting
Figure 3: Overview of Consis-GCPO framework. (a) Sampling workflow showing step-wise counterfactual interventions where prompt P or reference I, are selectively ablated at specific timesteps during SDE denoising, generating multiple trajectories for causal effect quantification. (b) Decoupled causal intervention at timestep t, illustrating how ablating prompt P or reference I, enables isolation of individual conditioning contributions through structural causal models. (c) Tem- poral importance re-weighting mechanism that transforms causal effects into normalized importance weights w/. (t') and wy, (t))
Definition 1 (Step-wise Causal Intervention). As shown in Figure 3 (b), step-wise causal in- tervention at timestep t' modifies only the transition at while maintaining standard conditions elsewhere:
so( t= (Slla, P, P,
do(C' = 2,(); T:-Ar
(5)
1
where C E (P, I.) represents the ablated condition.
4.1 DECOUPLED CAUSAL INTERVENTION TRAJECTORIES
For comprehensive causal analysis, we generate three types of trajectories for each initial noise 2!());
Main trajectory:
{ (9)
fo(x(() P.L.,e))
(6)
Prompl-intervention trajectory step t':
[o(a I,, t=t
(P.d [
(P.'.9) at-At
J=1
(p(() I. t+t
Reference-interveniin trajectory step t':
1o(a)1nl' P, t=t P.I,,)), (.), t
( (fr 9)
1=
fo(zfll
These intervention trajectories enable systematic analysis how each conditioning signal influences generation at different temporal stages.
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(a) Temporal Causal Weights
(b) Step-wise Intervention
Figure 4: Causal Diagnostic Analysis. (a) Statistical analysis reveals that text weights dominate early stages, while reference weights wr. peak in late stages. (b) Visual interventions confirm this:
early text ablation collapses structure, while late reference ablation degrades identity details.
4.1.1 QUANTIFYING TEMPORAL CAUSAL EFFECTS
We measure causal effects through specialized reward functions that evaluate different aspects of generation quality:
R vp(=))) ,), R v/r (x)) (0) I,),
where dhp measures text- text-image alignment and 1 evaluates visual consistency.
We quantify the instantaneous causal contribution of each modality timestep t' by measuring the performance degradation resulting from its intervention, as shown in Figure (c):
(P.'' a) P). (t') = R 9 pp(z
(t')
( (t' R| br, (m) 4 (9 I.).
(10)
Higher values indicate stronger causal dependence on the conditioning signal at that timestep.
4.1.2 TEMPORAL IMPORTANCE RE-WEIGHTING
We convert causal effects into normalized importance weights that capture temporal dynamics:
exp( (t))/T) E. exp(6 (t)/T) wT (1)( exp(6 (+))=
(1)
(1I)
Σ. exp(6,. (t)/T)
where 7 is temperature parameter controlling the sharpness of the distribution. These weights
explicitly reveal the temporal patterns of multi-modal influence during generation. We compute advantages that incorporate both temporal importance and group-level statistics:
Key Observations. These weights w(t) explicitly quantify the shifting reliance between modali- ties, resolving the uniform timestep bias in standard GRPO. As illustrated in Figure 4 our causal diagnosties reveal a distinct "Coarse-to-Fine statistical law:
Text Dominance (Early Steps): In high-noise stages - 1), the text weight (P(t) dominates. Visually, ablating the prompt here leads to structure collapse, confirming text drives global layout.
Reference Handover (Late Steps): In low-noise stages (t + 0), the reference weight wr. (1) takes over. Ablating the reference here preserves structure but loses identity details, confirming visual signals anchor fine-grained textures.
By incorporating these weights into the advantage calculation (Eq. 17), we ensure gradients are amplified precisely at the critical decision points for each modality.
R(g:
R(9) pr.
A (t') (o)(tt)
up OP
AQ
(g) (t')
(12)
01.

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Reference
Consist-GCPP
MS-Diffusion
UNO
DreamO
XVerse
bear Shanding on ourfaaand,
bright plainy with robot Tay.
the surfice the moen.
day chef's cooking tuble li lowi un
placed nable.
Figure 5: Qualitative comparison of image generation results. Consis-GCPO achieves state-of-the- art consistency and text fidelity in both single-reference and multi-reference image scenarios.
where pp M ) and op Std[[R())5 | are group statistics.
The total advantage combines both modalities:
((s) XpA (t') + Af (t'),
(13)
where Ap and X, are balancing coefficients that control the relative importance cach modality.
4.2 PROXIMAL POLICY OPTIMIZATION WITH CAUSAL WEIGHTING
The objective incorporates weighted advantages into proximal policy optimization framework:
1
Coomsis GCPO (0) =
(min(r?
G 8=1 v
(14)
where r. no (m
a1 bx
controls clipping for stability, and / weights the KI. regularization,
Pea (=
keeping the same as Eqn Although we aggregate objectives (L x Ap + A,.). disentanglement is achieved at the advantage estimation level. Since Ap and Ar, derive from independent counterfac- tuals (Eq, (T), their gradients are mathematically isolated. This formulation achieves targeted credit assignment by upweighting gradients at timesteps where each conditioning modality demonstrates high causal influence, leading to more efficient and interpretable multi-modaa modal alignment.
EXPERIMENTS
5.1 EXPERIMENTAL. SETUP
Model Architecture and Reward Configuration. We evaluate Consis-GCPO across image and video generation using unified framework. For image synthesis, employ UNO (Wu al. 2025)

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Table 1: Image quantitative comparison on DreamBench benchmark. The best results are in bold, and second-best are underlined. Ours includes standard deviation (#) over runs. indicatess statistical significance (p 0.05) against the best baseline.
Method
Subject
CLIP-T
CLIP-1 1
DINO 1
MS-DiE MS-Diffusion Wang ot al. 2025
Single
0.311
0.703
DreamO (Mou ct 2025
Single
0,306
0.760
Verse Chen ct 2025
Single
0.302
0.754
UNO (Wa et 2025
Single
0,304
0.760
UNO + FRo-GRPOO Ruiz el
2023
Single
0.314
0.839
0.766
UNO + Dance-GRPO
Li 2023
Single
0,301
0.772
Consis-GCPO
Single
0.325:t:.003"
0..00
0.701=0..004
MS-Difttsion WWang
2025
Multiple
0.319
0.726
0.525
DreamO (Mou
2025
Multiple
0.321
0.733
0.522
Veese Chen et 2025
Multiple
0.312
0.735
0.537
UNO (Wu et al.
2025
Multiple
0.322
0.733
0.542
UNO + Flow-GKPU Wu et al.
2025
Multiple
0.325
0.742
0.551
UNO + Dance-GRPO
Wu ct aL.
2025
Multiple
0.320
0.750
0.561
Consis-GCPO
Multiple
0.331-10.004
0.772:10.005
0.502:10.044
for single-reference and multi-subject scenarios, while video generation uses Vacc-113B (Jiang et al, 2025). For reward design, we implement domain-specific mechanisms targeting semantic alignment and visual consistency. For R21 generation, ImageReward (Xu et al.) 2023) serves as text-image aligmment evaluator Rp, while DINOv3 (Siméoni et al. (225) computes visual similarity between
reference and generated images Rr... For R2V generation, VideoAlign (Liu et al. 2025b) provides text-video alignment assessment as Rp, and DINOv3 processes sampled frames (initial, middle, final) for efficient R1, evaluation.
Datasets and Benchmarks. Training data combines Subject200K (Tan et al. 2024) and FFHQ (Karras al.) 2011),, with GPT generating 5,000 diverse text-image pairs covering various semantic concepts and visual styles. We evaluate on DreamBench (Ruiz et al. 2023) for image gen- eration and introduce Dream- "Bench-extenddng DreamBench subjects with GPT -enerratd action prompts-yielding 500 video test samples (with reward ablation and model efficient in Appendix).
Evaluation Protocol. Our comprehensive framework spans multiple dimensions: CLIP-T measures semantic alignment through cosine similarity between CLIP (Radford et al., 2021) text and image embeddings; CL.IP-I quantifies cross-modal consistency using CLIP visual features; DINO-1 (Oquab) et al. 2024) evaluates fine-grained visual similarity via ViT-S/16 features for identity preservation. For videos, we extend these to per-frame analysis and introduce Temporal Consistency, measuring framr- coherence through consecutive CLIP embedding similarities.
5.2 COMPARISON IMAGE CONSISTENCY GENERATION TASKS
We evaluate Consis-GCPO against state-of-the-art methods including MS-Diffusion (Wang et al., 2025), DreamO (oou et al. 2025), XVerse (Chen et al., 2025), UNO (Wu et al. 2025), and GRPO variants on DreamBench. Table demonstrates our method achieves superior performance across all metrics in both single-subject and multi subject scenarios, with particularly pronounced im- provements in complex multi-reference conditioning-obtaniiin and DINO (0.572) for multi-subject generation. Figure 5 provides qualitative validation, showing excellence instruction adherence while maintaining subject consistency. No- tably, only our method correctly generates the "standing" posture for the teddy bear rather than replicating the sitting reference pose, and uniquely captures the dynamic "chasing" behavior be- tween dog and toy car specified in prompts. These results demonstrate Consis-GCPOO capability for subject fidelity with precise text-following.
CLIP-T (0.331). CLIP-I (0.772),
5.3 EVALUATION ON VIDEO CONSISTENCY.
We compare Consis-GCPo against recent leading video generation frameworks, including
VACE (Jing al. 2025), MAGREF (Deng et al., 2025). Phantom (Liu al. 2025c), and Video-
Booth (Jiang et al. 2024). To provide a more comprehensive analysis, we also melude comparisons
with two reinforcement Tearning-base optimization strategies: Flow-GRPO (Liu al. 2025a) and
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shanding kitchen, preparing
gulden-hued bathes amber gow cheerful soft, through graws wogging subtly morning
has counter, Blender a plastic green leefy vegetables, plate
VAGE
Phantom
MAGREF
Figure 6: Qualitative comparison of video generation results. Consis-GCPO achieves superior subject consistency and temporal coherence while maintaining precise text alignment, demonstrating robust performance across diverse multi-subject scenarios with complex motions and interactions.
Table Video quantitative comparison for single- and multi-subject on Dream-VBende benchmark. The best results are in bold, and second-best underlined. Consis-GCPO includes standard deviation (+) over 5 runs. indicates statistical significance (p < 0.05) against the best baseline.
Methods
Subject CLIP-T t
CLIP-11
DINO-1
Consistency 1
VideoBooth
Jiang al.
2024
Single
0.267
0.523
0.634
0.938
MAGREF
[Deng ct al. 2025
Single
0.278
0.669
0.675
0.965
Phantom- 38
(T.u al.
2025e
Single
0.266
0.601
0.710
0.963
Vace-1.3B [iang at. 2025
Single
0.277
0.727
0.697
0.970
Vace-1.3B Flow-GRPO [Lu el al
2025a
Single
0.271
0.759
0.719
0.978
Vace-1.3B + DanceGRPO Xue al.
2025
Single
0.287
0.755
0,732
0,981
Consis-GCPO
Single
0.305-20044
0.790
0.74620.003*
0.984=0001
Vace-1.3B (Jang et al. 2025
Multiplc
0,274
0.615
0.589
0.966
Vace-1.3R Flow-GGRPU al. 2025a
Multiple
0.265
0.645
0.587
0.974
Vace-1.3B DanceGRPO
Multiple
0.281
0.642
0.594
0.972
Consis-GCPO
Multiple
0.300
0.67410.055
0.608
0.981
DanceGRPO (uue al. 2025). The evaluation considers three dimensions central to reference- guided video generation: (i) reference-video visual fidelity, (ii) text-video semantic alignment, and (ii) temporal consistency. Figure jiguuttoillustrrtes these improvements qualitatively. Quantitative results are summarized in Table A Our method achieves consistent improvements across all settings. In semantic alignment, Consis-GCPO attains a CLIP-T score of 0.305, which surpasses the strongest baseline DACCE-DancGGRPO: 0.287) by 6.3%. For identity preservation, our DINO-I score of 0.746 exceeds the prior best (0. 732), highlighting stronger reference fidelity. Most notably, our temporal consistency reaches 0.984, outperforming all variants including Flow-GRPO and Dance- GRPO, whose best results plateau at 0.978 and 0.981, respectively.
5.4 ANALYSIS ON STEP-WISE COUNTERFACTUAL INTERVENTIONS
To assess our decoupled causal intervention mechanism, we conduct ablation studies examining four configurations: (1) baseline Flow-GRPO without interventions, (2) prompt-only interventions do P = 9, t'), (3) reference-only interventions do(I, = 0. t'), and (4) our complete framework. Ta- ble 3 presents quantitative results for image and vidco generation. For multi-subject image genera- tion. baseline Flow-GRPO yields suboptimal performance (CLIP-T: 0.325, DINO-I: 0.551), demon- strating limitations of uniform temporal optimization. Prompt-only intervention improves text align-

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Table Ablation study on causal interventions in multi-subjeet R21 and R2V generation scenarios.
R2I Generation
R2V Generation
Method
CLIP-T
CLIP-I1
DINO-I
CLIP-T
CLIP-I1
DINO-I f
No Interventions (Flow-GRPO)
0.325
0.742
0.551
0.265
0.645
0.587
Prompt-only Interventions
0.338
0.736
0,544
0.310
0,628
0.556
Reference-only Interventions
0.322
0.780
0.570
0,255
0.670
0.615
Full Interventions (Ours) 0.331
0.772
0.572
0.300
0.674
0.608
ment (CLIP-T: 0.338) but shows marginal identity preservation gains (DINO-I: 0.544), indicating temporal credit assignment for textual conditioning alone is insufficient. Reference-only interven- tion significantly enhances visual consistency (CLIP-I: 0.780) while maintaining reasonable text alignment (CLIP-T: 0.322). Our complete framework outperforms all partial configurations (CLIP- T: 0.331, DINO-I: 0.572), empirically validating that independent temporal credit assignment for both conditioning modalities is essential for optimal subject-driven generation performance.
5.5 COMPARISON OF OPTIMIZATION STRATEGIES
To validate the design rationale discussed in Section 4.2
compare our Joint Optimization against two baselines: Alternating Optimization (updating text and image rewards alternately every steps) and Sequential Optimization (optimizing text first for 50% steps, then visual consistency).
As shown in Table 4 the Joint strategy yields superior performance and efficiency. The Alternating method suffers from gradient oscillation, resulting suboptimal convergence (DINO-I: 0.762). The Sequential approach exhibits catastrophic forgetting, evidenced by the sharp drop in text alignment (CLIP-T: 0.308) during the second phase. Furthermore, Joint Optimization 1.8x faster than alternating methods by sharing the backward pass, empirically confirming it as the Pareto-optimal choice for stability and training cost.
Table 4: Ablation on optimization strategies. Joint optimization achieves superior stability and efficiency compared alternating sequential methods.
Strategy
CLIP-T
CLIP-I
DINO-I f
Efficiency
Alternating (Text ++ Image)
0.317
0.837
0.762
1.8x (Slower)
Sequential (Text + Image) Ours (Joint)
0.308
0.842
0.770
1.5x (Slower)
0.325
0.848
0.781
1.0x
6 CONCLUSION
We presented Consis-GCPO, causal reinforcement learning framework that addresses fundamen- tal limitations in multimodal personalized generation. Through step-wise causal interventions, we enable precise quantification of when textual semantics versus visual references are most critical dur- advantage computation, transform instantaneous
ing denoising. Through remporally-weigheee causal effects into targeted optimization signals that decouple text and reference contributions. Our experiments demonstrate that Consis-GCPo achieves superior subject consistency while preserving strong fext-following capabilities, particularly excelling in complex multi-subject scenarios.
Limitations. While our method achieves significant improvements. current experiments focus algorithmic innovations rather than reward model enhancements. Future work will explore incor- porating multi-modal rewards from powerful foundation models to develop comprehensive reward frameworks for enhanced multi-dimensional performance.
Use of LLMs.. We utilize LLMs assist with formula derivations and writing refinement.
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Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Yao Huang, Shang- Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Ar- mand Joulin, and Piotr Bojanowski. Dinov2: Learning robust visual features without supervision. Trans. Mach. Learn. Res., 2024, 2024.
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Shaojin Wu, Mengqi Huang, Wenxu Wu, Yufeng Cheng, Fei Ding, and Qian He. Less-to- more generalization: Unlocking more controllability by in-context generation. arXiv preprint arXiv:2504.02166 2025.
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Zeyue Xue, Jie Wu, Yu Gao, Fangyuan Kong, Lingting Zhu, Mengzhao Chen, Zhiheng Liu, Wei Liu, Qiushan Guo, Weilin Huang, et al. Dancegrpo: Unleashing grpo on visual generation. arXiv preprint arXiv:2505.07811 2025.
Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. CoRR, abs/2308.06721, 2023.
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A APPENDIX
A.1 ADDITIONAL IMPLEMENTATION DETAILS
A.1.1 DATA PREPROCESSING PIPELINE
To ensure reproducibility and consistency with standard community protocols, we adopted a mini- mal preprocessing pipeline:
Visual Preprocessing: All images were resized such that the shortest side is 512, fol- lowed by center crop to 512 x 512 resolution. Pixel values were normalized the range [-1, 1]. No random augmentations (e.g., flipping. rotation) were applied during fine-tuning to maintain precise alignment between the prompt and visual content.
Text Preprocessing: Text prompts were tokenized using the standard CLIP tokenizer with context length of 77 tokens. Sequences exceeding this limit were truncated, and shorter ones were padded, consistent with the pre- pre-trained text encoder's requirements.
A.1.2 VIDEO DATA CONSTRUCTION PIPELINE
For video generation experiments, we constructed a specialized dataset of 5,000 motion-aware text- image pairs. We utilized the same visual references from Subject200K and FFHQ but synthe- sized dynamic temporal instructions. Representative examples demonstrating the dataset quality shown Figure
Motion-Aware Prompt Generation: We employed an automated pipeline using GPT-4 to act as a "Video Director." It converted static image captions into dynamic scripts by injecting temporal predicates (e.g., "turning," "running") and cinematic instructions (e.g., "zoom in," "pan right").
Generated prompts were filtered based on strict inclusion criteria: (1) Must contain at least one dynamic verb; (2) Length constraint of 20-50 words for concise- ness; (3) Semantic consistency with the reference object class.
Filtering Criteria:
Example: Static Input: "A girl smiling." + Dynamic Output: "Cinematic shot of girl slowly breaking into a warm smile, wind blowing her hair, 4k detail."
Reference Image
Source Datasel
R21 Caption
RIV Caption
A parked military helicopter with desert camo seheme, sitting on dusly
dramaicc actial view of the military helicopter banking sharply shrough a misty mountain valley. The side door is open maneusers past rocky clills, nashing by below: The camera follows the aiteralt as it accelerates towards bonizon under cloudy
Subjec1200K
bright daylighi, detailed mechanical textures.
pine
A detailed full-bodyy photograph of An underwater shot showing the penguin diving from an icc the standing upright surface blurzed snow backgroun.
shelf into clear blue water. swims gracefallyy using as
Subjec2200K
flipper, leaving down from the surfioe, illumisating the iey environment.
A medium-shot portrait of woman standing blooming garden, holding woven basket full of colorfal flowers She is facing smile, bothed warm afternoon sumlight.
medium shot as the woman slewly turs her head from looking oll-cmment dirccily facing the lens. She smiles gestly nods onee, sunlight filtering through a window, catching her hair.
FFHQ
Figure 7: Data samples illustrating our task-specific caption construction. We show repre- sentative reference images from different source datasets (FFHQ, Subjecr200K) alongside their corresponding textual conditions. The table contrasts the Static R2I Captions (focusing on visual appearance) with the Aotion-Aware R2V Captions (incorporating specific temporal dynamics and cinematic instructions).
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B ADDITIONAL ANALYSIS
B. SENSITIVITY ANALYSIS ON HYPERPARAMETERS
We conducted extensive ablation studies evaluate the robustness of our method to key hyperpa- rameters.
B.1.1 BALANCING COEFFICIENTS (Ap,Ar,).
We fixed Xp 1.0 and varied λy. As shown in Table 5 the balanced setting (Ap X = 1.0) achieves the optimal trade-off. Increasing λ1, yields dimmisshngg returns in identity metrics while significantly degrading text alignment.
Table 5: Impact of balancing coefficients. We report CLIP-I, CLIP-T and DINO-I to explicitly evaluate visual quality.
Ap
X1.
CLIP-T
CLIP-T
DINO-I 1
Avg. ?
1.0
0.5
0.332
0.835
0.760
0.642
1.0
1.0
0.325
0.848
0.781
0.651
1.0
1.2
0.318
0.850
0.783
0.650
1.0
2.0
0.305
0.852
0.788
0.648
B.1.2 TEMPERATURE PARAMETER (r).
Table presents the quantitative ablation on the softmax temperature The results confirm that 7 provides the optimal balance hetween temporal specialization and gradient density. Extreme 0.8 yields overly sparse gradients, while 1.2 leads to uniform weighting that mimics the baseline GRPO.
values degrade performance:
Visualization and Modality-Speciffe Analysis. To understand the underlying mechanism, we vi- sualize the learned temporal weight curves under different T settings in Figure
Impact of High Temperature (r 1.2): Increasing т overly smoothes the distribution.
As shown in the visualization, the distinct peak of reference guidance at late stages is flat- tened, causing the text weight to remain relatively high even when it should yield control. This **loss of modal discrimination ** prevents the model from focusing exclusively identity refinement, degrading DINO-I scores.
Impact of Low Temperature (r = 0.8): Conversely, decreasing t makes the distribution overly sharp. While it highlights the peak steps, forces the weights adjacent supportive steps to near zero. This **gradient sparsity** means the model receives no optimization signal for valid transitional timesteps, leading to unstable training and a drop in overall performance.
B.2 ANALYSIS REWARD MODEL
We further investigate the impact of different reward models video and image generation under the single-reference setting. As shown in Table 7 reward signals are divided into text-related (Rp) for semantic alignment and reference-related (R,,) for identity preservation. For Rp, VideoAlign achieves the highest average performance in video generation (0.614), whereas ImageReward is
Table Impact of temperature т. Extreme values (too sharp or too flat) degrade performance.
Type
CLIP-T
CLIP-I
DINO-I f
Avg. f
0.8
Sharp
0.316
0.839
0.765
0.640
1.0
Ours 0.325
0.848
0.781
0.651
1.2
Flat
0.319
0.845
0.772
0.645
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Text Weeght Waight wiff
Uee
Timedep :Dentrorg Process 1-01
( r 0.8
3.4
Weight Ret Werghtt wy1t
0.74
049
633
1200
(2) r = 1.0
Taxt Weight
01111
Ret
Wesge:
0.00
0.75
1.0
Timestos (Denakönng Process 10 (3)
1.2
Figure 8: Visualization of causal weights under different temperatures т. r = 1.0 (Ours) achieves clear separation of modal influence. 1.2 leads to over-smoothing (leakage), while т 0.8 leads to over-sharpening (information loss).
superior in image generation (0.651). CLIP-T attains the best CLIP-T score for video (0.322) but lags on other metrics. For R,, DINOv3 consistently outperforms CLIP-I and DINOv2, obtaining the highest averages in both video (0.614) and image (0.651). Therefore, we adopt VideoAlign for video generation and Image for image generation as Rr, together with DINOv3 as R,., since this combination provides the most favorable trade-off between semantic alignment and identity consistency.
Table 7: Ablation on reward models for video and image generation. For clarity we fix the best- performing Ry (or R,, and report comparisons by varying the other reward model. The best results are in bold, and the second-best are underlined.
R21 Generation
R2V Generation
Method
CLIP-T
CLIP-1
DINO-I1 Avg. 1
CLIPTT
CLIP-I1 1 DINO-1
Avg.
Rr (with R₁, DINOv3)
Qwen2,5-VL Qwen et al. CLIP-T RadooT Radford
0.303
0.779
0.727
0.603
0.297
0.738
0.704
0.580
0.324
0.710
0.700
0.578
0.322
0.723
0.711
0.585
ImagelReward Nu el a 217
0.325
0.848
0.781
0.651
VideoAligu Liu at 20230
0.305
0.790
0.746
0.614
Ry, (with R,
ImageReward
R,
Vide R2V)
CLIPI- Radford al. 2021
0.861
0.694
0.612
0.279
0.795
0.691
0.588
DINOv2 Oquab et al.
201A
0.291
0.829
0.786
0.635
0.288
0.720
0.782
0.597
DINOv3 Simeom ct al
2025
0.325
0.848
0.781
0.651
0.305
0.790
0.746
0.614
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Image-tex. text Reward
Image-image Reward
Consis- AinsisGGRAPO Flow-GRPO
0.6
1.0
0.8
0.21
0.6
step
200 steps
0 step
200 steps
Figure 9: Training reward trajectories of FlowGRPO and Consis-GCPO.
B.3 ANALYSIS MODEL EFFICIENCY
We assess the efficiency of the proposed framework by analyzing both computational cost and re- ward evolution, illustrated in Figure and Figure TO
B.3.1 TRAINING REWARD TRAJECTORIES.
As shown in Figure 9 we examine the trajectories of text-related and reference-related rewards during training for FlowGRPO and Consis- Our results show that, in later training stages, the strong semantic alignment characteristic of FlowGRPO constrains consistency improvements and even introduces detrimental side effects. In contrast, Consis-GRPOO effectively circumvents these issues by decoupling semantic adherence from consistency optimization. Furthermore, under an identical training budget of 200 steps, Consis-GRPO achieves considerably higher reference-related rewards compared to FlowGRPO, indicating superior consistency in generation.
B.3.2 COMPUTATIONAL EFFICIENCY AND CONVERGENCE ANALYSIS
To investigate the benefits of our timestep-aware optimization, we provide comprehensive analysis of the trade-off between per-step computational cost and overall convergence efficiency.
Mechanism: Inference vs. Training Cost. While our method involves calculating counterfactual trajectories, these additional rollouts are performed in no-grad mode (inference only). The com- putationally expensive backpropagation is performed only on the main trajectory. Therefore, the gradient computation overhead remains comparable to standard fine-tuning. Our method effectively trades "cheap" inference compute for "expensive" training iterations.
Sample Efficiency and Total Convergence Time. By extracting high-quality causal gradients, Consis-GCPO achieves dramatic improvement in sample efficiency compared to the baseline:
Baseline (Flow-GRPO): Requires R 15, 000 steps to converge.
Ours (Consis-GCPO): Converges in x 1,300 steps.
Despite the increased inference load per step (approximately 8x for trajectory rollout), this 11.5x reduction required training steps results in significant net gain.
Wall-Clock Speedup. We further analyze the evaluation rewards normalized by GPU hours to measure real-world efficiency. As presented in Figure I0 Consis-GCPP attains the target evaluation reward approximately 1.4x faster than Flo--GPOO in terms of wall-clock time. This demonstrates that the proposed causal intervention accelerates convergence without compromising performance, validating our design choice of investing in causal gradient quality.
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Gara Row GAPO
Eval Reward
1 4x faster
12 GPU Hours
8
16
Figure 10: Evaluation rewards vs. GPU hours. Consis-GRPO reaches the same reward level about 1.4x faster than FlowGRPO, highlighting its improved computational efficiency.
C MORE VISUALIZATION
C.I PROMPTS IN FIGURE 1.
The prompts in Figure are as follows:
1. In a park, A man is holding a camera shotting.
cartoon character chasing toy car across the surface of the moon, under natural daylight, with realistic details of lunar soil, craters, and the vast space backdrop. An anime space ranger is riding bicycle in front of a cyberpunk skyscraper background.
4. A cinematic scene of a man wearing a t-shirt outdoors petting a dog, warm sunlight, detailed textures, natural background.
5. The anime Spider-Man leaps across skyserapers, clutching roll of film.
A cinematic 4K video of a young woman gracefully playing the violin a grand, opulent concert hall adorned with golden chandeliers and ornate decorations. The camera captures multiple angles in smooth motion: close-up shots her hands moving the bow across the strings, mid-shots of her calm and focused expression, and wide shots revealing the majestic hall with its glittering lights and luxurious atmosphere.
7. A cute small dog wearing red Christmas hat lies cozily glowing fireplace. The warm firelight flickers softly on the walls, creating a festive and comforting holiday atmosphere. After moment of resting, the dog slowly gets up, shakes its body gently, and begins to walk forward.
fluffy rug in front of
C.2 FAILURE CASES
We have identified two primary limitations where method faces challenges, as visualized in Figure
Extreme Semantic Conflict: When the text prompt and visual reference are fundamentally "A cat", Reference: [Image of a dog) ), the causal reweighting mechanism struggles to reconcile the divergence. High causal effects are detected for both conflicting modalities simultaneously. which confuses the optimization and often results in hybrid artifacts or semantic oscillation.
contradictory (e.g., Prompt:
In scenarios involving extremely small subjects within aide-angle shots (c.g., tiny face in a crowd), the underlying reward models (DINO/CLIP) sometimes fail capture identity loss accurately due to resolution limits. Consequently, even if our
Micro-Detal Loss:
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method correctly upweights the relevant timesteps, the reward signal itself is too noisy to guide the recovery of micro-details.
Failure Case text prompt and visual reference are fundamentally contradictory.
A dog leaps into the air, paws outstretched toward dangling red yarn ball.
Failure Casc 2 extremely small subjects in widc-angle shots.
A man stands on bustling city street dusk, one hand holding can of beer.
Visualization failure cases. (Left) Hybrid artifacts resulting from extreme semantic conflict between prompt and reference. (Right) Loss of micro-details in wide-angle shots due to sparse reward signals.
Figure 11:
C.3 ADDITIONAL RESULTS
We further provide supplementary results on consistency generation. Figure 12 and Figure 13 presents examples of image-level consistency, while Figure 14 illustrates video-level consistency, demonstrating that our Consis-GCPO maintains coherent and stable outputs across both modalities.
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In the moonlit jungle, a white tiger prowls silently, its eye catching the glint an old, abandoned watch nearby.
A deer standing beside a vintage van.
woman an men are sitting together, with beer can the table between them.
vintage television on, there's cocktail and donut the table beside it.
bay standing open field, with eogle soaring the sky above him and dag sitting at his feet.
A street lamp illuminates an Eevee figurine placed next to piggy bank on the sidewalk.
woman with cap playing puppy.
A boy and man are watching hamster play in cage.
Figure 12: More R2I Generation results.
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A hot air balloon floating in the sky above sneaker lying on the ground.
An Eevee figurine placed inside leather handbag.
rubber duck floating near a tree- like character by small pond.
A man is using hair dryer.
A man holding a vintage camera.
An elephant is carrying backpack its back.
A wolf is standing beside a teapot.
A boy holding bowl filled with rice,
Figure 13: More R2I Generation results.
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small cat dressed in flowing purple wizard robe sits at a wooden table under the soft glow of candlelight. The cat gazes intently at an ancient open book, its eyes reflecting the flickering flames. After brief pause, the gently raises its paw and page,
video begins with close-up of woman passionately singing on brightly stage, her hand gripping the microphone. She is wearing shimmering silver sequin slip dress that sparkles under the intensified stage lights. The camera slowly pulls back, revealing colorful spotlights.
The video shows speaking reporters locker He is wearing blue shirt appears addressing the media. The background features shelves filled with various sports equipment, including hockey gear such as helmets and gloves.
golden retriever sits calmly gently ruffled by morning breeze, while behind a quaint blue house stands washed in the soft, golden light of sunrise, its windows reflecting faint glimmers of the awakening day."
Figure 14: More R2V Generation results.
21


Reviews:
[{"review_id": "4u4psgzkTB", "reviewer": "Reviewer_1Guz", "summary": "This paper addresses the core conflict in personalized generation: balancing subject consistency (visual fidelity) with semantic alignment (following the text prompt). The authors' key insight is that this is a temporal problem: the text prompt matters most in the early denoising steps to set the scene, while the visual reference matters most in the late steps to lock in details.", "questions": "- Your method seems to imply 3x the rollouts per step. Can you clarify the actual sampling overhead? How does that square with the 1.4x wall-clock speedup you show in Figure 7?\n\n- You measure the temporal importance of text vs. visuals. I'd love to see a graph of these weights ($\\omega_P(t)$ and $\\omega_{I_r}(t)$) over the denoising timesteps $t$. This would be the ultimate proof of your hypothesis!\n\n- How sensitive is the model to the balancing coefficients ($\\lambda_P$, $\\lambda_{I_r}$)? Was it hard to find a good balance?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Identifying the temporal nature of the text-vs-visuals conflict is a brilliant insight.\n\n- The \"causal intervention\" approach is a smart, non-hand-wavy way to measure the actual contribution of each modality at each step.\n\n- The results are fantastic. The qualitative images (like the \"standing\" bear in Fig 4) show it's really following the prompt while holding the subject's identity. It also wins quantitatively on both images and video (Tables 1, 2).\n\n- As a bonus, Figure 7 shows it converges 1.4x faster (in GPU hours) than the baseline.", "weaknesses": "The main concern is computational cost. The method seems to require running multiple full-denoising rollouts for each training step to get the causal effects. The paper's claim of a 1.4x speedup (Fig 7) feels contradictory to this apparent 3x increase in work. This trade-off needs to be made much clearer.", "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": "GFuHZgTTzs", "reviewer": "Reviewer_AUFx", "summary": "This paper proposes **Consis-GCPO**, a causal reinforcement learning framework for subject-driven image and video generation. The key innovation is reformulating multi-modal conditional generation through discrete-time causal modeling with **step-wise causal interventions**. \n\nUnlike existing GRPO methods (Flow-GRPO, DanceGRPO) that apply uniform optimization across all denoising timesteps, Consis-GCPO (1) introduces **decoupled causal intervention trajectories** that selectively **ablate text prompts or visual references** at specific timesteps, and (2) quantifies **instantaneous causal effects** to measure when textual vs. visual conditions are most critical. These effects are further converted into temporally-weighted advantages for targeted optimization.", "questions": "1. How exactly is \"ablating\"  $P$ or $I_r$ implemented? Is it by setting it to an empty string/zero tensor? Or by using an unconditional model/learned zero embedding?\n\n2. **Sensitivity Analysis**: How sensitive is the method to the temperature parameter $\\tau$ in Eq. 11? And Can you provide error bars and statistical significance tests for all metrics in Tables 1-2?\n\n3. Can the authors provide some **failure cases** and discuss when the method fails or performs poorly?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. **Well-motivated problem**: a fundamental limitation in existing GRPO methods—\"temporal blindness\" to how text and visual conditioning vary in importance during denoising.\n\n2. **Strong empirical results**: The experimental results show consistent improvements across all metrics, particularly notable in multi-subject scenarios (*e.g.*, CLIP-I: 0.772 vs. 0.750 for Dance-GRPO).\n\n3. Table 3 effectively demonstrates the necessity of both prompt and reference interventions.", "weaknesses": "1. I'm concerned about the causal identification assumptions. In my view, the text and image conditions are likely **not causally independent**, they interact through the model's attention mechanisms. In Equation (5), ablating $P$ or $I_r$ at timestep $t'$ is presented as a causal intervention $\\text{do}(C=∅, t')$, but what ensures this ablation isolates the causal effect rather than just correlation?", "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": "Q2FLPBbzUW", "reviewer": "Reviewer_6q4d", "summary": "This work primarily addresses the limitations in the subject-driven generation domain, where current methods struggle to simultaneously ensure object fidelity and text alignment. The authors first identify the inherent issues of traditional GRPO algorithms and propose targeted solutions, namely decoupled causal intervention and temporally-weighted advantage computation mechanisms. These innovations culminate in the Consis-GCPO framework, which successfully resolves the existing challenges. The proposed approach demonstrates significant improvements over state-of-the-art personalized generation methods, achieving superior subject consistency while preserving strong text-following capabilities.", "questions": "- How was the data preprocessed? \n- Why was the FFHQ dataset used for image tasks, considering it is focused on facial data?\n-  What dataset was used for video tasks?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 3, "strengths": "- The inherent issues of directly transferring GRPO to the subject-driven generation domain have been identified. \n- Consis-GCPO demonstrates advantages over Flow-GRPO and Dance-GRPO in both qualitative and quantitative experiments.", "weaknesses": "- The article is not well-written in certain areas. For instance, in line 275, the relationship between this sentence and Figure 3(c) is unclear. Additionally, the caption for Figure 3 does not correspond correctly with the subfigures, leading to confusion. In line 180, the sentence structure is convoluted, and the logical relationship is not clearly articulated, causing semantic confusion. Furthermore, there is a missing space between words in line 239.\n- The challenge of measuring the degree to which an action influences the final reward, especially in the long term, is a classic problem in reinforcement learning, and has led to many elegant solutions. In this paper, the use of $\\delta _{P/I_r}^{(g)}(t^`)$ to indicate the causal dependence on the conditioning signal at that timestep is a rather naive and imprecise estimate, as it only represents a single Monte Carlo sample.\n- The paper claims: \"Our key insight is that different conditioning signals exert varying influence throughout the denoising process—text guides semantic structure in early steps while visual references anchor details in later stages\", is there any theoretical or experimental evidence to support this claim?", "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": "ksgiLgu72u", "reviewer": "Reviewer_XhRE", "summary": "This paper proposes a new online reinforcement learning framework for personalized generation by performing causal modeling that provides temporally weighted advantages for optimization at each denoising timestep. Experiments on DreamBench and the newly proposed Dream-VBench demonstrate that the method achieves better performance than existing frameworks in both personalized image and video generation tasks.", "questions": "The questions are mainly about the weaknesses:\n\n1. Could the authors explain in more detail how the proposed method resolves the entangled feedback issue, given that the final optimization still uses a single scalar reward?\n2. Why is the proposed causal weighting expected to provide a more reliable temporal signal for the reward, and how does it avoid the uniform timestep bias present in GRPO?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper is clearly written and well-structured, with informative figures such as the challenge overview (Figure 2) and the training pipeline (Figure 3).\n* Introducing a causal framework for computing weighted advantages is both novel and insightful. The causal interpretation of multi-modal conditioning (text and visual reference) adds a principled perspective to reinforcement learning–based generation.\n* The experiments are extensive, including both image and video generation. The introduction of Dream-VBench further validates the generalizability of the method across modalities.", "weaknesses": "* One key idea of the paper is to *entangle feedback* from text and image references. However, the proposed method still computes a single reward by summing the weighted advantages from both modalities. This seems to reduce the degree of entanglement. A more entangled approach might involve alternately fixing one modality and computing separate rewards for the other, then combining them to guide optimization.\n* The main method section could benefit from a deeper explanation of how the causal effect estimation directly contributes to solving the *temporal blindness* problem of previous GRPO methods. The framework is sound, but the \"why\" behind the causal weighting’s effectiveness would be clearer with an intuitive example or a brief theoretical justification.\n\n---\n\n**Overall**: The paper presents an interesting and timely idea, and the experimental results are strong, with comprehensive analysis and solid baselines. Although the method description could be clearer in connecting causal modeling to the core problem formulation, the contribution and potential impact are evident. Therefore, I lean toward accepting the paper.", "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)