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

2026-02-07

AI Summary

This work addresses the challenges of high inference costs, temporal error accumulation, and weak feedback encountered when deploying pretrained video generation models. To this end, we propose a diagnostic-driven, three-stage post-training alignment framework that systematically enhances perceptual fidelity, temporal consistency, and instruction-following capability. The framework integrates supervised policy shaping, reward-driven reinforcement learning, and preference fine-tuning in sequence, augmented with stability constraints to mitigate degradation during iterative refinement. Experimental results demonstrate that our approach significantly improves generation quality and robustness while preserving model controllability, offering a scalable post-training paradigm for efficient and stable production-level deployment of video generation models.

Technology Category

• Computer Vision: Large Vision Models
• Natural Language Processing: Generation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• video generation
• post-training
• controllability
• temporal coherence
• robustness

Innovation

• post-training
• video generation
• reinforcement learning
• temporal coherence
• controllability