EasyVideoR1: Easier RL for Video Understanding

Hugging Face Daily Papers Papers
reinforcement-learning video-understanding vision-language-models rlvr multimodal open-source training-framework

Summary

EasyVideoR1 is an efficient reinforcement learning framework for training large vision-language models on video understanding tasks, featuring offline preprocessing with tensor caching for 1.47x throughput improvement, a task-aware reward system covering 11 problem types, and evaluation across 22 video benchmarks. It also supports joint image-video training and a mixed offline-online data training paradigm.

Reinforcement learning from verifiable rewards (RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large language models. As models evolve into natively multimodal architectures, extending RLVR to video understanding becomes increasingly important yet remains largely unexplored, due to the diversity of video task types, the computational overhead of repeatedly decoding and preprocessing high-dimensional visual inputs, and the difficulty of reproducible evaluation across numerous sensitive hyperparameters. Existing open-source RL training frameworks provide solid infrastructure for text and image scenarios but lack systematic optimizations tailored for video modality. In this work, we present EasyVideoR1, a complete and efficient reinforcement learning framework specifically designed for training large vision-language models on video understanding tasks. EasyVideoR1 makes the following contributions: (1) a full video RL training pipeline with offline preprocessing and tensor caching that eliminates redundant video decoding and yields a 1.47 times throughput improvement; (2) a comprehensive, task-aware reward system covering 11 distinct video and image problem types with unified routing and modular extension; (3) a mixed offline-online data training paradigm that combines curated high-quality trajectories with on-policy exploration, benefiting the learning of more challenging tasks; (4) joint image-video training with independently configurable pixel budgets, allowing the two modalities to mutually reinforce each other; and (5) an asynchronous multi-benchmark evaluation framework covering 22 mainstream video understanding benchmarks, with reproduced accuracy closely aligned with officially reported scores.
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Source: https://huggingface.co/papers/2604.16893

Abstract

EasyVideoR1 presents an efficient reinforcement learning framework for video understanding that improves training throughput, supports diverse video tasks, and enables joint image-video training with comprehensive evaluation across multiple benchmarks.

Reinforcement learning from verifiable rewards(RLVR) has demonstrated remarkable effectiveness in improving the reasoning capabilities of large language models. As models evolve into natively multimodal architectures, extending RLVR tovideo understandingbecomes increasingly important yet remains largely unexplored, due to the diversity of video task types, the computational overhead of repeatedly decoding and preprocessing high-dimensional visual inputs, and the difficulty of reproducible evaluation across numerous sensitive hyperparameters. Existing open-source RL training frameworks provide solid infrastructure for text and image scenarios but lack systematic optimizations tailored for video modality. In this work, we present EasyVideoR1, a complete and efficient reinforcement learning framework specifically designed for traininglarge vision-language modelsonvideo understandingtasks. EasyVideoR1 makes the following contributions: (1) a full video RL training pipeline withoffline preprocessingandtensor cachingthat eliminates redundant video decoding and yields a 1.47 times throughput improvement; (2) a comprehensive, task-awarereward systemcovering 11 distinct video and image problem types with unified routing and modular extension; (3) amixed offline-online data trainingparadigm that combines curated high-quality trajectories with on-policy exploration, benefiting the learning of more challenging tasks; (4)joint image-video trainingwith independently configurable pixel budgets, allowing the two modalities to mutually reinforce each other; and (5) anasynchronous multi-benchmark evaluationframework covering 22 mainstreamvideo understandingbenchmarks, with reproduced accuracy closely aligned with officially reported scores.

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