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ARXIV:2604.24953 · GENERATIVE IMAGE/VIDEO · SUBMITTED 29 APR · 20:25 UTC · FRESHNESS STALE
ARXIV:2604.24953GENERATIVE IMAGE/VIDEOSUBMITTED 29 APR · 20:25 UTCFRESHNESS STALEMing Li · Jie Wu · Justin Cui · Xiaojie Li · Rui Wang · Chen Chen · arXiv
Scales visual preference optimization for generative models by introducing a novel adaptive algorithm and a massive, high-quality preference dataset.
Opportunity summary
Pain Scales visual preference optimization for generative models by introducing a novel adaptive algorithm and a massive, high-quality preference dataset.
Evidence 0 refs | 4 sources | 50% coverage
Blocker Evidence unverified
Scales visual preference optimization for generative models by introducing a novel adaptive algorithm and a massive, high-quality preference dataset. Current open-source preference datasets contain conflicting preference patterns, where winners excel in some dimensions but…
While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open-source preference datasets contain conflicting preference patterns, where winners excel in some dimensions but…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. On noisy datasets like Pick-a-Pic V2, Poly-DPO achieves 6.87 and 2.32 gains over Diffusion-DPO on GenEval for SD1.5 and SDXL, respectively. A public repository…
Generative Image/Video moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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Scales visual preference optimization for generative models by introducing a novel adaptive algorithm and a massive, high-quality preference dataset.
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10.48550/arXiv.2604.24953Scales visual preference optimization for generative models by introducing a novel adaptive algorithm and a massive, high-quality preference dataset.
Abstract
While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open-source preference datasets contain conflicting preference patterns, where winners excel in some dimensions but underperform in others. Naively optimizing on such noisy datasets fails to learn preferences, hindering effective scaling. To enhance robustness against noise, we propose Poly-DPO, which extends the DPO objective with an additional polynomial term that dynamically adjusts model confidence based on dataset characteristics, enabling effective learning across diverse data distributions. Beyond biased patterns, existing datasets suffer from low resolution, limited prompt diversity, and imbalanced distributions. To facilitate large-scale visual preference optimization by tackling data bottlenecks, we construct ViPO, a massive-scale preference dataset with 1M image pairs at 1024px across five categories and 300K video pairs at 720p+ across three categories. State-of-the-art generative models and diverse prompts ensure reliable preference signals with balanced distributions. Remarkably, when applying Poly-DPO to our high-quality dataset, the optimal configuration converges to standard DPO. This convergence validates dataset quality and Poly-DPO's adaptive nature: sophisticated optimization becomes unnecessary with sufficient data quality, yet remains valuable for imperfect datasets. We validate our approach across visual generation models. On noisy datasets like Pick-a-Pic V2, Poly-DPO achieves 6.87 and 2.32 gains over Diffusion-DPO on GenEval for SD1.5 and SDXL, respectively. For ViPO, models achieve performance far exceeding those trained on existing open-source preference datasets. These results confirm that addressing both algorithmic adaptability and data quality is essential for scaling visual preference optimization.
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PROBLEM
Scales visual preference optimization for generative models by introducing a novel adaptive algorithm and a massive, high-quality preference dataset. Current open-source preference datasets contain conflicting preference patterns, where winners excel in some dimensions but under...
METHOD
While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open-source preference datasets contain conflicting preference patterns, where winners excel in some dimensions but underpe...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. On noisy datasets like Pick-a-Pic V2, Poly-DPO achieves 6.87 and 2.32 gains over Diffusion-DPO on GenEval for SD1.5 and SDXL, respectively. A public repository is linked, so build verification can inspect...
WHY NOW
Generative Image/Video moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
Scales visual preference optimization for generative models by introducing a novel adaptive algorithm and a massive, high-quality preference dataset. Current open-source preference datasets contain conflicting preference patterns, where winners excel in some dimensions but underperform in others.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
While preference optimization is crucial for improving visual generative models, how to effectively scale this paradigm remains largely unexplored. Current open-source preference datasets contain conflicting preference patterns, where winners excel in some dimensions but underperform in others.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. On noisy datasets like Pick-a-Pic V2, Poly-DPO achieves 6.87 and 2.32 gains over Diffusion-DPO on GenEval for SD1.5 and SDXL, respectively. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Generative Image/Video moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Scales visual preference optimization for generative models by introducing a novel adaptive algorithm and a massive, high-quality preference dataset.
Segment
Generative Image/Video
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7.0/10 public viability
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