Enterprise AI Analysis
Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy
Current state-of-the-art reward models (RMs) struggle to capture nuanced human preferences due to limitations in existing preference datasets. Skywork-Reward-V2 addresses this by introducing SynPref-40M, a large-scale, high-quality preference dataset curated through a novel human-AI synergistic pipeline. This approach enables the development of versatile RMs that achieve state-of-the-art performance across critical evaluation benchmarks.
Executive Impact: Key Metrics & Breakthroughs
Skywork-Reward-V2 represents a significant leap forward in AI alignment, driven by a novel human-AI data curation strategy. Our analysis reveals these critical metrics:
Total Preference Pairs Curated
State-of-the-Art Benchmarks
Reward Models Released
Data Quality Improvement (Ablation)
Deep Analysis & Enterprise Applications
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Data Curation Pipeline
Reward Model Performance
Human-AI Synergy
Ablation Insights
The core of Skywork-Reward-V2's success lies in its innovative two-stage preference data curation pipeline. This pipeline effectively combines human annotation for unparalleled quality with LLM-guided automatic curation for massive scalability. It ensures that the resulting SynPref-40M dataset is not only large but also rigorously high-quality, addressing the brittleness seen in previous reward models.
Skywork-Reward-V2 demonstrates superior performance across a diverse suite of benchmarks, outperforming much larger and more established models. This highlights the critical role of data quality over sheer model size. The models show strong capabilities in general human preferences, objective correctness, resistance to stylistic biases, safety, and best-of-N scaling.
The pipeline leverages the complementary strengths of human annotators—providing verified, high-quality labels under stringent protocols—and large language models (LLMs)—performing automatic, human-guided curation at scale. This synergy is key to overcoming the limitations of previous datasets, which were often narrow, synthetically labeled, and lacked rigorous quality control.
Ablation studies confirm that the effectiveness of our approach stems not only from data scale but crucially from high-quality curation. Simple LLM curation alone yields minimal gains, while human curation, especially with preference attributes and external tools, drives significant improvements. Adaptive retrieval further boosts LLM curation quality.
SynPref-40M Data Curation Pipeline
Stage 1: Small-Scale Human-in-the-Loop Curation
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Iterative RM Training & Adaptive Retrieval
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Stage 2: Large-Scale Automatic LLM Curation
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Final Curated Data Pool (SynPref-40M)
Our two-stage pipeline combines human verification for quality and LLM-guided automation for scalability, iteratively refining the dataset and reward model to achieve high-quality preference data at scale.
| Model | RewardBench | JudgeBench | Avg. |
|---|---|---|---|
| Skywork-Reward-V2-Llama-3.1-8B-40M | 97.8 | 83.4 | 88.6 |
| Skywork-Reward-V2-Llama-3.1-8B | 96.4 | 80.0 | 85.7 |
| INF-ORM-Llama3.1-70B | 95.1 | 70.2 | 73.5 |
| Llama-3.1-Nemotron-70B | 93.9 | 65.8 | 71.6 |
| Skywork-Reward-Gemma-2-27B-v0.2 | 94.3 | 66.5 | 71.6 |
Skywork-Reward-V2 models consistently outperform existing open reward models across major benchmarks, including those with significantly larger parameter counts. This demonstrates the superior quality of our curated preference data.
Average Score Across 7 Benchmarks (Skywork-Reward-V2-Llama-3.1-8B-40M)
Case Study: Advancing LLM Alignment with SynPref-40M
Our work highlights a significant advancement in LLM alignment by focusing on the quality and scale of preference data. The SynPref-40M dataset, with its 40 million meticulously curated preference pairs, is a testament to the power of human-AI synergy. This rigorous curation process, involving both human verification and LLM-guided automatic labeling, has enabled Skywork-Reward-V2 to achieve state-of-the-art performance, demonstrating that high-quality data is paramount for robust reward models. This approach not only enhances existing open reward models but also sets a new standard for preference data curation in the field of RLHF.
Conclusion: By prioritizing data quality and leveraging a human-AI pipeline, we've unlocked new levels of performance and versatility in reward modeling, pushing the boundaries of what's achievable in LLM alignment.
Improvement from Full Human Annotation Protocol (Avg. Score)
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Your AI Implementation Roadmap
A phased approach to integrate Skywork-Reward-V2 and similar advanced AI solutions, ensuring a smooth transition and maximized ROI.
Phase 1: Discovery & Strategy
Assess current RM effectiveness, identify key areas for preference data improvement, and define custom annotation protocols. Establish initial human-AI curation workflows.
Phase 2: Pilot Curation & Model Training
Deploy the human-AI synergistic pipeline on a small scale. Train initial Skywork-Reward-V2 models using curated seed data and evaluate performance against internal benchmarks.
Phase 3: Large-Scale Data Expansion
Scale up data curation using LLM-guided automatic methods, continuously incorporating feedback from human verification. Retrain and refine reward models with the expanded SynPref-40M-like dataset.
Phase 4: Integration & Optimization
Integrate refined Skywork-Reward-V2 models into existing RLHF pipelines. Monitor performance, conduct further ablations, and adapt models to evolving organizational needs and preference distributions.
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