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…

Autoregressive visual generation models have emerged as a groundbreaking approach to image synthesis, drawing inspiration from language model token prediction mechanisms. These innovative models utilize image tokenizers to transform visual content into discrete or continuous tokens. The approach facilitates flexible multimodal integrations and allows adaptation of architectural innovations from LLM research. However, the field has…