Open-OmniCoach: Evidence-Enhanced Trajectory Learning for Omni Video Agents

Training capable omni video agents requires more than diverse question-answer pairs: agents must learn to reliably find, connect, and correct task-relevant evidence in omni-setting. However, we argue that existing data pipelines provide limited control over task-relevant evidence and weak supervision for these capabilities. In this work, we present Open OmniCoach, a new framework for evidence-seeking omni video agents. It contains a high-quality dataset, OmniCoach-240k, and a new agentic reinforcement learning (RL) method, Step-Level Evidence-Guided Policy Optimization (SEPO). For the former, OmniCoach-340k has both multi-span trajectory data and reflection trajectory data. We design a three-stage data pipeline by leveraging both fine-grained omni-evidence and the agent's own errors. Meanwhile, compared with other RL baselines, SEPO provides turn-level credit based on improvements in evidence coverage and temporal localization. Applying both trajectory data and the SEPO method, we obtain Open OmniAgent models. The resulting models outperform the baselines across multiple video and audio-visual understanding tasks by significant margins, demonstrating the effectiveness of the Open OmniCoach framework. In particular, Open OmniAgent-30B improves average accuracy over Qwen3-Omni by 6.3 percentage points across nine benchmarks, with gains of 12.2 and 13.3 points on LVOmniBench and LVBench, respectively. Code, datasets, and models will be available to the community.