arXiv:2605.02395v2 Announce Type: replace 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 steps under the corrupted state, and verifies that the injected step is not derivable from its pr
The increasing reliance on Process Reward Models (PRMs) in AI and the inherent limitations of current data generation methods are driving the need for more robust synthesis techniques.
Improving the control and verifiability of synthetic process data directly enhances the reliability, safety, and performance of advanced AI systems, particularly autonomous agents.
AI developers can now generate higher-quality, more specific training data for PRMs, enabling finer-tuned control over AI behavior and easier debugging of autonomous systems.
- · AI developers
- · Companies deploying AI agents
- · AI safety researchers
- · Autonomous systems sector
- · Developers reliant on manual error injection
- · Systems with opaque error attribution
More robust and reliable AI agents are developed due to better process supervision.
Accelerated development and adoption of AI systems in critical applications where verifiable behavior is paramount.
Increased trust in AI autonomy leading to broader societal integration of agentic systems.
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Read at arXiv cs.AI