Think, then Score: Decoupled Reasoning and Scoring for Video Reward Modeling
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| Authors | Yuan Wang et al. |
| Year | 2026 |
| HF Upvotes | 2 |
| arXiv | 2605.05922 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Recent advances in generative video models are increasingly driven by post-training and test-time scaling, both of which critically depend on the quality of video reward models (RMs). An ideal reward model should predict accurate rewards that align with human preferences across diverse scenarios. However, existing paradigms face a fundamental dilemma: Discriminative RMs regress rewards directly on features extracted by multimodal large language models (MLLMs) without explicit reasoning, making them prone to shortcut learning and heavily reliant on massive data scaling for generalization. In contrast, Generative RMs with Chain-of-Thought (CoT) reasoning exhibit superior interpretability and generalization potential, as they leverage fine-grained semantic supervision to internalize the rationales behind human preferences. However, they suffer from inherent optimization bottlenecks due to the coupling of reasoning and scoring within a single autoregressive inference chain. To harness the generalization benefits of CoT reasoning while mitigating the training instability of coupled reasoning and scoring, we introduce DeScore, a training-efficient and generalizable video reward model. DeScore employs a decoupled ``think-then-score'' paradigm: an MLLM first generates an explicit CoT, followed by a dedicated discriminative scoring module consisting of a learnable query token and a regression head that predicts the final reward. DeScore is optimized via a two-stage framework: (1) a discriminative cold start incorporating a random mask mechanism to ensure robust scoring capabilities, and (2) a dual-objective reinforcement learning stage that independently refines CoT reasoning quality and calibrates the final reward, ensuring that higher-quality reasoning directly translates to superior model performance.
Engineering Breakdown
Plain English
This paper proposes a decoupled approach to video reward modeling that separates reasoning from scoring—the reward model first generates explicit reasoning about why a video is good or bad (via chain-of-thought), then uses that reasoning to assign a numerical score. The key finding is that this two-stage approach outperforms traditional discriminative models that directly regress rewards from video features, achieving better generalization and interpretability without requiring massive datasets.
Key Engineering Insight
Decoupling reasoning from scoring in reward models forces the system to build explicit semantic understanding rather than learning shortcuts on feature correlations. This matters because it makes the model's decisions auditable and dramatically improves how well the model generalizes to new video styles and scenarios it hasn't seen during training.
Why It Matters for Engineers
Video generation models are now being scaled at test-time and during post-training, which means reward model quality directly impacts the quality of generated videos and training efficiency. Current discriminative reward models either require enormous datasets or fail on out-of-distribution content—this approach solves that by building interpretable reasoning steps, reducing both data requirements and failure modes in production systems.
Research Context
Reward modeling for video generation has relied on discriminative approaches that are data-inefficient and opaque. This paper advances the field by showing that generative chain-of-thought reasoning (borrowed from language model scaling) can be adapted to multimodal video understanding, enabling better reward signals for reinforcement learning pipelines used in modern video synthesis systems.
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