Controllable and Verifiable Process Data Synthesis for Process Reward Models
📰 ArXiv cs.AI
Learn to synthesize controllable and verifiable process data for process reward models, enabling better error control and trajectory consistency
Action Steps
- Construct a correct symbolic reasoning chain using a knowledge graph
- Inject a template-aware error into an intermediate step to simulate real-world errors
- Recompute subsequent steps to maintain trajectory consistency
- Verify the synthesized data using a validation metric
- Apply the synthesized data to train a process reward model
Who Needs to Know This
Data scientists and AI engineers working on process reward models can benefit from this framework to improve the quality of their supervision data
Key Insight
💡 Controllable and verifiable process data synthesis enables better error control and trajectory consistency in process reward models
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🤖 Synthesize controllable process data for better PRMs! 📈
Key Takeaways
Learn to synthesize controllable and verifiable process data for process reward models, enabling better error control and trajectory consistency
Full Article
Title: Controllable and Verifiable Process Data Synthesis for Process Reward Models
Abstract:
arXiv:2605.02395v1 Announce Type: new Abstract: Process reward models (PRMs) rely on high-quality process supervision data, yet existing construction methods often provide limited control over error location, error type, and trajectory consistency. We propose a controllable and verifiable framework for synthesizing process supervision data for PRMs. Our framework first constructs a correct symbolic reasoning chain, injects a template-aware error into an intermediate step, recomputes subsequent s
Abstract:
arXiv:2605.02395v1 Announce Type: new Abstract: Process reward models (PRMs) rely on high-quality process supervision data, yet existing construction methods often provide limited control over error location, error type, and trajectory consistency. We propose a controllable and verifiable framework for synthesizing process supervision data for PRMs. Our framework first constructs a correct symbolic reasoning chain, injects a template-aware error into an intermediate step, recomputes subsequent s
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