Exploring the Effect of Reinforcement Learning on Video Understanding:
  Insights from SEED-Bench-R1
Authors: name: Yi Chen, name: Yuying Ge, name: Rui Wang, name: Yixiao Ge, name: Lu Qiu, name: Ying Shan, name: Xihui Liu

Recent advancements in Chain of Thought (COT) generation have significantly
improved the reasoning capabilities of Large Language Models (LLMs), with
reinforcement learning (RL) emerging as an effective post-training approach.
Multimodal Large Language Models (MLLMs) inherit this reasoning potential but
remain underexplored in tasks requiring both perception and logical reasoning.
To address this, we introduce SEED-Bench-R1, a benchmark designed to
systematically evaluate post-training methods for MLLMs in video understanding.
It includes intricate real-world videos and complex everyday planning tasks in
the format of multiple-choice questions, requiring sophisticated perception and
reasoning. SEED-Bench-R1 assesses generalization through a three-level
hierarchy: in-distribution, cross-environment, and cross-environment-task
scenarios, equipped with a large-scale training dataset with easily verifiable
ground-truth answers. Using Qwen2-VL-Instruct-7B as a base model, we compare RL
with supervised fine-tuning (SFT), demonstrating RL's data efficiency and
superior performance on both in-distribution and out-of-distribution tasks,
even outperforming SFT on general video understanding benchmarks like
LongVideoBench. Our detailed analysis reveals that RL enhances visual
perception but often produces less logically coherent reasoning chains. We
identify key limitations such as inconsistent reasoning and overlooked visual
cues, and suggest future improvements in base model reasoning, reward modeling,
and RL robustness against noisy signals.