Process-supervised reward models (PRMs) offer fine-grained, step-wise feedback on model responses, aiding in selecting effective reasoning paths for complex tasks. Unlike output reward models (ORMs), which evaluate responses based on final outputs, PRMs provide detailed assessments at each step, making them particularly valuable for reasoning-intensive applications. While PRMs have been extensively studied in language tasks,…

LLMs have shown impressive capabilities in reasoning tasks like Chain-of-Thought (CoT), enhancing accuracy and interpretability in complex problem-solving. While researchers are extending these capabilities to multi-modal domains, videos present unique challenges due to their temporal dimension. Unlike static images, videos require understanding dynamic interactions over time. Current visual CoT methods excel with static inputs but…

Large Vision-Language Models (LVLMs) have made significant strides in recent years, yet several key limitations persist. One major challenge is aligning these models effectively with human expectations, particularly for tasks involving detailed and precise visual information. Traditionally, LVLMs undergo a two-stage training paradigm: pretraining followed by supervised fine-tuning. However, supervised fine-tuning alone cannot fully overcome…