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

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 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.