TL;DR: This article traces four generations of the Qwen-VL family. The original Qwen-VL established visual-language alignment and a three-stage training pipeline. Qwen2-VL added dynamic resolution, M-RoPE, and video input. Qwen2.5-VL focused on inference efficiency, temporal video modeling, and post-training data quality. Qwen3-VL then moved visual-language fusion deeper into the model.
TL;DR: This article follows the main line of positional encoding research: the limitations of learned absolute embeddings in BERT and GPT, sinusoidal encoding in the original Transformer, the improvements introduced by relative positional encoding in models such as T5 and Transformer-XL, and finally Rotary Positional Encoding (RoPE). RoPE applies an absolute rotation at each position, yet the resulting attention inner product depends only on relative distance. Later extensions, including 2D-RoPE and M-RoPE, carry the same mechanism into vision and multimodal models such as Qwen2-VL and Qwen3-VL. The question throughout is how a model represents distance while preserving length extrapolation, KV caching, and multimodal coordinates.
TL;DR: Reinforcement learning runs through the post-training stage of RLHF for large language models. PPO from OpenAI, DPO from Stanford, and GRPO from DeepSeek have each changed how LLMs are trained after pre-training. This article covers their objectives, derivations, and implementations, then connects the design choices that led from one method to the next.
Paper: CapRL: Stimulating Dense Image Caption Capabilities via Reinforcement Learning (CVPR 2025) Authors: Xing et al. TL;DR: CapRL turns caption quality into a verifiable question: if a caption is complete enough, a text-only LLM should be able to answer image-related multiple-choice questions from that caption alone. This setup avoids the length and style preferences of LVLM-as-a-Judge and gives RL training a more stable reward signal.
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