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

TL;DR
TeleBoost introduces a comprehensive post-training framework that enhances the fidelity, controllability, and robustness of video generators by integrating policy shaping, reinforcement learning, and refinement under stability constraints.
Contribution
It presents a unified, staged optimization framework for post-training video generation models, addressing practical constraints and improving stability and performance.
Findings
Improved perceptual fidelity and temporal coherence.
Enhanced controllability and adherence to prompts.
Stable and scalable post-training pipeline.
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…
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Taxonomy
TopicsHuman Pose and Action Recognition · Reinforcement Learning in Robotics · Human Motion and Animation
