arXiv Computer Vision October 5, 2026 By Zhaolu Kang, Shiyu Liu, Tailong Luo, Wei Zhang, Yingjie He, Lei Wei, Guansu Wang, Liang He, Siheng Wang, Guangyuan Dong, Jiaqi Su, Shuang Chen, Haoyu Ji, Qishi Zhan, Kaiyue Zhou

Behavior Pack Optimization for Video MLLM Post-Training

Read the original on arXiv Computer Vision →

The paper introduces Behavior Pack Optimization (BPO), a post‑training method for video multimodal large language models that replaces single‑response rewards with a set of outputs across counterfactual views. BPO enforces stability when interventions are irrelevant, sensitivity when key evidence is removed, and abstention when no evidence remains, using an anchor‑relative advantage to keep the objective stable with small pack sizes. Experiments on datasets such as TempCompass, MVBench, and NExT‑QA show that BPO improves macro accuracy and abstention metrics for models like Qwen2.5‑VL‑7B‑Instruct, with gains that transfer to other benchmarks and models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv Computer Vision

Aug 24

Annotations as Rollouts: Efficient and Scalable Reinforcement Learning for Video MLLMs

The paper introduces OraRL, a reinforcement learning framework that leverages annotations as oracle rollouts to improve sample efficiency and scalability for video multimodal large language models (MLLMs). By decoupling advantage estimation and employing sign‑balanced pruning, OraRL achieves faster training and better performance across multiple video‑perception benchmarks compared to existing methods. The approach scales from 0.8B to 9B parameters and handles up to 100k prompts, delivering significant gains in temporal mIoU, tracking accuracy, segmentation, and spatial‑intelligence metrics.

By Yunheng Li, Guohong Mu, Hao Li, Shengsheng Qian, Dingwen Zhang, Qibin Hou, Ming-Ming Cheng

llms computer-vision reinforcement-learning fine-tuning efficiency multimodal benchmarks

arXiv Computer Vision

5d ago

Token-Level Video Reinforcement Learning

arXiv:2610.01973v1 Announce Type: new Abstract: Reinforcement learning (RL) for video generation usually assigns one scalar reward to an entire sampled video. Yet a video is not uniformly flawed: som...

By Yifan Wang, Gordon Guocheng Qian, Yanyu Li, Anil Kag, Yun Fu

llms diffusion reinforcement-learning multimodal

arXiv AI

Jun 24

video-SALMONN-R$^3$: Learning to ReWatch, ReAsk, and ReAnswer for Efficient Video Understanding

arXiv:2606. 24477v1 Announce Type: cross Abstract: Video large language models (LLMs) are often constrained by computation and memory budgets, leading them to use reduced frame rates and spatial resolutions, which may cause them to miss critical information for question answering (QA).

By Yixuan Li, Guangzhi Sun, Yudong Yang, Wei Li, Zejun MA, Chao Zhang

llms nlp reinforcement-learning fine-tuning

arXiv AI

Jun 11

ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward

arXiv:2606. 11209v1 Announce Type: cross Abstract: Visual question answering increasingly requires multi-step reasoning.

By Jingpei Wu, Xiao Han, Weixiang Shen, Boer Zhang, Zifeng Ding, Volker Tresp

llms nlp reinforcement-learning fine-tuning multimodal benchmarks

arXiv Computer Vision

Aug 24

Enhancing Localized Reasoning for Long Video Understanding via Efficient Segment-to-Video Supervision

arXiv:2608.20814v1 Announce Type: new Abstract: Though Multimodal Large Language Models (MLLMs) have shown impressive potential in video understanding, long video understanding (LVU) remains challeng...

By Beibei Zhang, Chao Xu, Jun Lan, Zongyi Li, Lai Wei, Huijia Zhu, Tongwei Ren

llms reinforcement-learning fine-tuning efficiency multimodal benchmarks

arXiv Computation and Language

Sep 1

Multi-Agent Self-Improving Reinforcement Learning for Video Reasoning

arXiv:2608.28675v1 Announce Type: cross Abstract: Video reasoning tasks such as grounded video question answering and temporal grounding require selecting temporal evidence that supports the query. I...

By Mingwen Zhang, Jisheng Dang, Minqiang Yang, Bimei Wang, Bin Hu, Tat-Seng Chua

agents nlp reinforcement-learning fine-tuning benchmarks

One email a morning, machine-written

Related stories

Annotations as Rollouts: Efficient and Scalable Reinforcement Learning for Video MLLMs

Token-Level Video Reinforcement Learning

video-SALMONN-R$^3$: Learning to ReWatch, ReAsk, and ReAnswer for Efficient Video Understanding

ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward

Enhancing Localized Reasoning for Long Video Understanding via Efficient Segment-to-Video Supervision

Multi-Agent Self-Improving Reinforcement Learning for Video Reasoning