OmniVChat introduces a synthetic data pipeline (OmniVChat-Studio), benchmark (OmniVChat-Bench), and RL reward design (OmniVChat-RL) to train multimodal models for native audio-visual dialogue, achieving strong generalization from synthetic to real human interactions.
This article reviews Kronos, a unified and scalable pre‑training framework designed for financial K‑line data, detailing its tokenization approach, autoregressive architecture, large‑scale pre‑training on 12 billion records, and experimental results that show substantial gains in price prediction, volatility forecasting, synthetic data generation, and investment simulation.
The paper introduces SICOG, a three‑stage collaborative framework that combines post‑training enhancement, inference optimization, and re‑pretraining with a self‑generated data loop, allowing large multimodal models to continuously improve without massive human‑annotated datasets, and demonstrates consistent gains across dozens of benchmarks.
