Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1 (2503.24376v1)

Abstract: Recent advancements in Chain of Thought (COT) generation have significantly improved the reasoning capabilities of LLMs, with reinforcement learning (RL) emerging as an effective post-training approach. Multimodal LLMs (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.

Insights into Reinforcement Learning for Video Understanding: An Examination of SEED-Bench-R1

The paper "Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1" investigates the significance and potential of reinforcement learning (RL) in enhancing video understanding capabilities of multimodal LLMs (MLLMs). With advancements in MLLMs, particularly in reasoning and perception tasks, the authors have introduced SEED-Bench-R1, a comprehensive benchmark designed to evaluate post-training methods across varied video understanding scenarios.

Key Components of SEED-Bench-R1

SEED-Bench-R1 is meticulously structured to assess models on in-distribution, cross-environment, and cross-environment-task scenarios, employing a diverse set of real-world egocentric videos. The benchmark employs a hierarchical evaluation approach to rigorously test generalization abilities. The dataset features a large-scale training set complemented by validation sets bifurcated into three levels:

1. Level-1 (L1): Captures quotidian activities within familiar environments akin to the training dataset, serving as an in-distribution evaluation.
2. Level-2 (L2): Introduces new environments, enhancing robustness assessment in cross-environment scenarios.
3. Level-3 (L3): Encompasses a broader range of tasks across varied domains, challenging models to adapt to cross-environment-task scenarios.

Experimental Observations

The authors employed Qwen2-VL-Instruct-7B as the base model, contrasting RL approaches such as Group Relative Policy Optimization (GRPO) with supervised fine-tuning (SFT). The experiments revealed:

• Data Efficiency: RL demonstrated superior data efficiency, outperforming SFT in both in-distribution and out-of-distribution tasks. Particularly noteworthy was RL's performance in OOD scenarios, including cross-task challenges.
• Generalization Strength: The RL model exhibited remarkable generalization capabilities, achieving higher performance on general video benchmarks like LongVideoBench.

Detailed Analysis

While RL significantly improved visual perception, it revealed shortcomings in logical coherence of reasoning chains. RL's outcome-supervised reward iteratively refined attention to visual cues but did not substantially enhance emergent reasoning abilities. This indicates a persistent challenge in aligning visual perception with logical inference seamlessly.

Limitations and Future Directions

The utilization of simple reward signals occasionally compromised logical coherence, suggesting potential improvements in reward modeling to enhance reasoning transparency and robustness to noise. Future explorations may focus on:

• Pre-RL Reasoning Elicitation: Cultivating enhanced reasoning abilities prior to RL, leveraging high-quality chain-of-thought (COT) data.
• Refined Reward Design: Incorporating process-based rewards to supervise reasoning rationality and foster a balanced approach to perception and reasoning.

Besides optimizing RL methods for scalability with larger datasets, tackling perceptual limitations dictated by sampling constraints remains crucial. Achieving effective alignment across multimodal understanding endeavors will likely rest on advancing these methodologies.

Conclusion

The paper offers illuminating perspectives on the efficacy of RL in video understanding tasks. While SEED-Bench-R1 establishes a rigorous testbed, the paper highlights current limitations in achieving full multimodal alignment, paving pathways for future research in the interplay between reasoning and perceptual enhancement in MLLMs.