TeleBoost: A Systematic Alignment Framework for High-Fidelity, Controllable, and Robust Video Generation

• URL: http://arxiv.org/abs/2602.07595v1
• Date: Sat, 07 Feb 2026 15:49:25 GMT
• Title: TeleBoost: A Systematic Alignment Framework for High-Fidelity, Controllable, and Robust Video Generation
• Authors: Yuanzhi Liang, Xuan'er Wu, Yirui Liu, Yijie Fang, Yizhen Fan, Ke Hao, Rui Li, Ruiying Liu, Ziqi Ni, Peng Yu, Yanbo Wang, Haibin Huang, Qizhen Weng, Chi Zhang, Xuelong Li,
• Abstract summary: Post-training is the decisive step for converting a pretrained video generator into a production-oriented model.<n>This report presents a systematical post-training framework that organizes supervised policy shaping, reward-driven reinforcement learning, and preference-based refinement.
• Score: 45.864084191741135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Post-training is the decisive step for converting a pretrained video generator into a production-oriented model that is instruction-following, controllable, and robust over long temporal horizons. This report presents a systematical post-training framework that organizes supervised policy shaping, reward-driven reinforcement learning, and preference-based refinement into a single stability-constrained optimization stack. The framework is designed around practical video-generation constraints, including high rollout cost, temporally compounding failure modes, and feedback that is heterogeneous, uncertain, and often weakly discriminative. By treating optimization as a staged, diagnostic-driven process rather than a collection of isolated tricks, the report summarizes a cohesive recipe for improving perceptual fidelity, temporal coherence, and prompt adherence while preserving the controllability established at initialization. The resulting framework provides a clear blueprint for building scalable post-training pipelines that remain stable, extensible, and effective in real-world deployment settings.

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