StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding
Summary
StreamOPD enhances streaming video understanding through a post-training recipe using on-policy distillation and spatio-temporal cue gating, achieving significant benchmark improvements without requiring inference-time memory.
View Cached Full Text
Cached at: 08/18/26, 07:53 PM
Paper page - StreamOPD: A Post-Training Recipe with Spatio-Temporal Cue Gating for Streaming Video Understanding
Source: https://huggingface.co/papers/2608.16320
Abstract
StreamOPD improves streaming video understanding via on-policy distillation with verifiable rewards and a spatio-temporal cue-gating mechanism, achieving near-teacher performance without inference-time memory.
Streaming video understanding demands direct responses from the causally observed prefix of an unfolding video. Existing systems add inference-time memory, retrieval, and compression, yet a training-free sliding-window baseline already matches them. We therefore fix a memory-free recent-window protocol and ask how far post-training alone can go. Reinforcement learning withverifiable rewardsfits this regime poorly, encouraging long ``think-then-answer’’ generations, whileon-policy distillation(OPD) supplies dense token-level teacher supervision on student trajectories but is stable only when both models train inthinking mode. These observations lead toStreamOPD, a recipe combining verifiable streaming-video data, thinking-mode OPD, and instruct-mode deployment. It raises StreamingBench from 77.9% to 83.9%---within 0.3 points of the 9B teacher---and improves OVO-Bench excluding its hallucination-detection subtask (HLD) by 9.1 points under unchanged inference. As a teacher-privilege extension,Spatio-Temporal CueGate(ST-CueGate) aggregates cue-versus-no-cue teacher likelihood ratios into a group-relative response score that reweights OPD. It reaches 71.9% on OVO-Bench (excluding HLD) and 64.9% on Video-MME, and is the only variant that stays above the base model on all four benchmarks. Replacing the teacher with a frozen copy of the student’s initial policy---on-policy self-distillation---retains most of these gains and lifts HLD to 57.0%, above both the untrained student and the 9B teacher, soabstention lossis not intrinsic to the recipe. We provide a transparent and reproducible reference for open-source streaming-video research.
View arXiv pageView PDFProject pageGitHub4Add to collection
Models citing this paper1
#### UniX-Lab/StreamOPD-4B-ST-CueGate Video-Text-to-Text• 5B• Updatedabout 17 hours ago • 9
Datasets citing this paper0
No dataset linking this paper
Cite arxiv.org/abs/2608.16320 in a dataset README.md to link it from this page.
Spaces citing this paper0
No Space linking this paper
Cite arxiv.org/abs/2608.16320 in a Space README.md to link it from this page.
Collections including this paper0
No Collection including this paper
Add this paper to acollectionto link it from this page.
Similar Articles
OPSD-V: On-Policy Self-Distillation for Post-Training Few-Step Autoregressive Video Generators
OPSD-V improves few-step autoregressive video diffusion models by using real long-video data as temporal context during training, providing dense trajectory-level supervision that enhances visual quality and motion dynamics without altering inference mechanisms.
Simple-OPD: Demystifying Warm-up for On-policy Distillation
This paper investigates the warm-up stage for on-policy distillation (OPD), showing that teacher-compatible chain-of-thought supervision and LoRA-based training with near-saturation duration improve OPD effectiveness. It introduces Simple-OPD, a plug-and-play initialization method that boosts OPD performance across diverse settings.
D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models
This paper introduces D-OPSD, a novel training paradigm for step-distilled diffusion models that enables on-policy self-distillation during supervised fine-tuning. It allows models to learn new concepts or styles without compromising their efficient few-step inference capabilities.
DanceOPD: On-Policy Generative Field Distillation
DanceOPD proposes an on-policy generative field distillation framework for flow-matching models that unifies text-to-image generation, local editing, and global editing via capability-specific routing and velocity-based training, improving multi-capability composition while preserving anchor generation quality.
On-policy distillation: one of the hottest terms on PapersWithCode [R]
Hugging Face's Niels introduces On-policy Distillation (OPD), a key post-training technique used in models like Qwen 3.6/3.7, GLM-5.1, and DeepSeek-V4, now featured on PapersWithCode with a linked whiteboard explanation by Sasha Rush and Dwarkesh Patel.