Author: Ricardo

Vision Language Models (VLMs) allow both text inputs and visual understanding. However, image resolution is crucial for VLM performance for processing text and chart-rich data. Increasing image resolution creates significant challenges. First, pretrained vision encoders often struggle with high-resolution images due to inefficient pretraining requirements. Running inference on high-resolution images increases computational costs and latency…

Reinforcement Learning with Verifiable Rewards (RLVR) allows LLMs to perform complex reasoning on tasks with clear, verifiable outcomes, with strong performance in mathematics and coding. However, many real-world scenarios lack such explicit verifiable answers, posing a challenge for training models without direct reward signals. Current methods address this gap through RLHF via preference ranking, where…