pith. sign in

arxiv: 2601.05729 · v2 · pith:UUNRMKTQnew · submitted 2026-01-09 · 💻 cs.CV

TAGRPO: Boosting GRPO on Image-to-Video Generation with Direct Trajectory Alignment

Jin Wang , Jianxiang Lu , Guangzheng Xu , Comi Chen , Haoyu Yang , Linqing Wang , Peng Chen , Mingtao Chen
show 5 more authors
Zhichao Hu Longhuang Wu Shuai Shao Qinglin Lu Ping Luo
This is my paper
classification 💻 cs.CV
keywords tagrpogenerationgrpomodelsalignmentdirectimage-to-videoimprovements
0
0 comments X
read the original abstract

Recent studies have demonstrated the efficacy of integrating Group Relative Policy Optimization (GRPO) into flow matching models, particularly for text-to-image and text-to-video generation. However, we find that directly applying these techniques to image-to-video (I2V) models often fails to yield consistent reward improvements. To address this limitation, we present TAGRPO, a robust post-training framework for I2V models inspired by contrastive learning. Our approach is grounded in the observation that rollout videos generated from identical initial noise provide superior guidance for optimization. Leveraging this insight, we propose a novel GRPO loss applied to intermediate latents, encouraging direct alignment with high-reward trajectories while maximizing distance from low-reward counterparts. Furthermore, we introduce a memory bank for rollout videos to enhance diversity and reduce computational overhead. Despite its simplicity, TAGRPO achieves significant improvements over DanceGRPO in I2V generation. The deliverables will be updated at https://tagrpo.github.io/ .

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. CreFlow: Corrective Reflow for Sparse-Reward Embodied Video Diffusion RL

    cs.CV 2026-05 conditional novelty 7.0

    CreFlow combines LTL compositional rewards with credit-aware NFT and corrective reflow losses in online RL to improve embodied video diffusion models, raising downstream task success by 23.8 percentage points on eight...

  2. CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating

    cs.CV 2026-05 unverdicted novelty 7.0

    CaC is a hierarchical spatiotemporal concentrating reward model for video anomalies that reports 25.7% accuracy gains on fine-grained benchmarks and 11.7% anomaly reduction in generated videos via a new dataset and GR...

  3. Reward-Aware Trajectory Shaping for Few-step Visual Generation

    cs.CV 2026-04 unverdicted novelty 5.0

    RATS lets few-step visual generators surpass multi-step teachers by shaping trajectories with reward-based adaptive guidance instead of strict imitation.

  4. Image-to-Video Diffusion: From Foundations to Open Frontiers

    cs.CV 2026-05 unverdicted novelty 3.0

    A survey that organizes diffusion image-to-video methods into a taxonomy, distills core designs in condition encoding, temporal modeling, noise prior, and upsampling, and discusses applications plus challenges.