Reward models are central to Reinforcement Learning from Human Feedback, yet current open-source variants struggle to mirror the full range of human preferences. Even with advanced training strategies, meaningful advances have been scarce. A key issue lies in the preference datasets, which often cover too few scenarios, rely on synthetic examples, or lack rigorous quality checks. Simple rule-based systems may handle math or coding, but they miss more subtle judgments.
Popular benchmarks such as RewardBench no longer reflect actual reward model performance, showing weak links to downstream results. Teams often depend on human reviewers to collect preference labels, but that process demands significant time, budget, and can yield inconsistent outputs.
To scale up annotations, researchers have turned to RLAIF, where large language models generate comparisons that sometimes outperform human judgments. Hybrid methods now merge AI-generated data with verified labels. In parallel, reward modeling has moved beyond basic schemes like the Bradley-Terry model to include generative and direct-optimization approaches.
A collaboration between 2050 Research and Skywork AI produced SynPref-40M, a collection of 40 million preference pairs assembled through a two-phase human-AI workflow. Expert annotators validate sample quality, and LLMs extend coverage under their supervision. From this pool, the team selected a top subset of 26 million pairs to train a suite called Skywork-Reward-V2.
Skywork-Reward-V2 comprises eight models ranging from 0.6 billion to 8 billion parameters. Training used both Qwen3 (0.6B–8B) and Llama 3.1/3.2 (1B–8B) backbones. Each variant achieved leading scores on seven major benchmarks—including RewardBench, PPE, RM-Bench, and JudgeBench—excelling in alignment, safety, objectivity, and bias resilience.
The development process features two main stages. In the first phase, human-verified labels guide an LLM to score diverse preference attributes. The team then iterates through training cycles and error analysis to refine the reward signal, helping the model learn subtle distinctions in user expectations.
During the second phase, the top-performing model is pitted against a human-trained “gold” reward system. Samples are filtered by comparing ratings from both sources, keeping only those that meet a consistency threshold. This removes the need for extra human review and retains high-quality pairs at scale.
Comparisons showed that modestly sized models like Qwen3-1.7B could outperform some 70 billion-parameter systems when trained on the refined SynPref-40M subset. The Llama-3.1-8B variant led with an average benchmark score of 88.6, demonstrating that careful data curation and efficient training can outweigh sheer parameter count in real-world RLHF tasks.
Looking ahead, the team plans to explore new training strategies as reward models take on an increasingly vital role in shaping LLM behavior. Their findings underline data quality and curation workflows as the primary drivers of performance rather than simply scaling up model size.
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