VideoLatent: Video-Language Learning via Latent Self-Forcing

• URL: http://arxiv.org/abs/2606.22870v1
• Date: Mon, 22 Jun 2026 05:29:30 GMT
• Title: VideoLatent: Video-Language Learning via Latent Self-Forcing
• Abstract summary: VideoLatent is a novel MLLM equipped with a latent injection module tailored for video understanding and reasoning.<n>Our model consistently outperforms existing standard and latent MLLMs on general video understanding and complex video reasoning.
• Score: 44.01259508852106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in chain-of-thought (CoT) reasoning have shown promise in enhancing video understanding and reasoning capabilities of multimodal large language models (MLLMs). However, existing CoT-based MLLMs require labor-intensive CoT annotations and incur substantial training and inference overhead. While visual latent reasoning has emerged as a more efficient alternative, existing methods primarily focus on image tasks and heavily rely on additional supervision signals for visual latent generation (e.g., CoT traces, auxiliary images, or fine-grained annotations), limiting their scalability and transferability to video tasks. To bridge this gap, we introduce VideoLatent, a novel MLLM equipped with a latent injection module tailored for video understanding and reasoning. Specifically, VideoLatent learns to perform visual latent reasoning using a new latent self-forcing training paradigm, which comprises latent alignment and latent diversity objectives, and relies solely on standard video-question-answer triplets. Extensive experiments across 14 benchmarks demonstrate that our model consistently outperforms existing standard and latent MLLMs on general video understanding and complex video reasoning. Compared with Video-R1, our VideoLatent achieves superior computational efficiency, reducing training/inference overhead by $\sim$6$\times$/$\sim$68$\times$. Moreover, experiments demonstrate that our method has strong generalizability to different MLLM backbones and different model scales.

Related papers

• VidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval [11.519642157641023]
  This paper focuses on leveraging MLLMs for video-text embedding and retrieval.<n>We first conduct a systematic layer-wise analysis, showing that intermediate (pre-trained) MLLM layers already encode substantial task-relevant information.<n>We demonstrate that combining intermediate-layer embeddings with a calibrated MLLM head yields strong zero-shot retrieval performance without any training.
  arXiv  Detail & Related papers  (2026-02-08T19:39:32Z)
• Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models [78.32948112203228]
  Video understanding represents the most challenging frontier in computer vision.<n>Recent emergence of Video-Large Multitemporal Models has demonstrated remarkable capabilities in video understanding tasks.<n>Survey aims to provide researchers and practitioners with a unified framework for advancing Video-LMM capabilities.
  arXiv  Detail & Related papers  (2025-10-06T17:10:44Z)
• Reasoning-Enhanced Domain-Adaptive Pretraining of Multimodal Large Language Models for Short Video Content Governance [34.134289344567705]
  We propose a reasoning-enhanced multimodal large language model (MLLM) pretraining paradigm for unified inappropriate content detection.<n>To address the distribution gap between short video content and the original pretraining data of MLLMs, we introduce three targeted pretraining tasks.<n> Experimental results show that our pretraining approach significantly improves the MLLM's performance in both zero-shot and supervised fine-tuning settings.
  arXiv  Detail & Related papers  (2025-09-25T19:46:34Z)
• Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning [39.6349428129868]
  multimodal large language models (MLLMs) are crucial for downstream tasks like video question answering and temporal grounding.<n>We propose Video Intelligence via Tool-Augmented Learning (VITAL), a novel end-to-end agentic video reasoning framework.<n>With a visual toolbox, the model can densely sample new video frames on demand and generate multimodal CoT for precise long video reasoning.
  arXiv  Detail & Related papers  (2025-08-06T13:03:21Z)
• Reinforcement Learning Tuning for VideoLLMs: Reward Design and Data Efficiency [56.475612147721264]
  We propose a dual-reward formulation that supervises both semantic and temporal reasoning through discrete and continuous reward signals.<n>We evaluate our approach across eight representative video understanding tasks, including VideoQA, Temporal Video Grounding, and Grounded VideoQA.<n>Results underscore the importance of reward design and data selection in advancing reasoning-centric video understanding with MLLMs.
  arXiv  Detail & Related papers  (2025-06-02T17:28:26Z)
• STEP: Enhancing Video-LLMs' Compositional Reasoning by Spatio-Temporal Graph-guided Self-Training [87.58996020705258]
  Video Large Language Models (Video-LLMs) have recently shown strong derivation in basic video understanding tasks.<n>Video-LLMs struggle with compositional reasoning that requires multi-step explicit-temporal inference across object relations, interactions and events.<n>We propose STEP, a novel graph-guided self-training method that enables VideoLLMs to generate reasoning-rich finetuning data from any raw videos to improve itself.
  arXiv  Detail & Related papers  (2024-11-29T11:54:55Z)
• VURF: A General-purpose Reasoning and Self-refinement Framework for Video Understanding [65.12464615430036]
  This paper introduces a Video Understanding and Reasoning Framework (VURF) based on the reasoning power of Large Language Models (LLMs)<n>Ours is a novel approach to extend the utility of LLMs in the context of video tasks, leveraging their capacity to generalize from minimal input and output demonstrations within a contextual framework.
  arXiv  Detail & Related papers  (2024-03-21T18:00:00Z)
• VideoLLM: Modeling Video Sequence with Large Language Models [70.32832021713864]
  Existing video understanding models are often task-specific and lack a comprehensive capability of handling diverse tasks. We propose a novel framework called VideoLLM that leverages the sequence reasoning capabilities of pre-trained LLMs. VideoLLM incorporates a carefully designed Modality and Semantic Translator, which convert inputs from various modalities into a unified token sequence.
  arXiv  Detail & Related papers  (2023-05-22T17:51:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.