Controllable and Verifiable Process Data Synthesis for Process Reward Models

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

A new framework synthesizes controllable and verifiable process supervision data for Process Reward Models (PRMs), addressing limitations in existing methods regarding error control and trajectory consistency. The framework constructs a correct symbolic reasoning chain, injects a template-aware error into an intermediate step, recomputes subsequent steps under the corrupted state, and verifies the error's non-derivability. This generates paired prefix-invalid but trajectory-consistent data, translated into natural language for PRM training. Experiments with Llama-3.1-8B and Qwen-2.5-7B show the synthesized data improve Best-of-8 reranking on logical reasoning, with average scores rising from 0.528 to 0.591 for Llama and 0.567 to 0.615 for Qwen. The data also transfer to mathematical reasoning and highlight the challenge of first-error localization.

Key takeaway

For Machine Learning Engineers developing or fine-tuning Process Reward Models, you should consider integrating synthetically generated, verifiable process supervision data. This approach, which injects controlled errors and recomputes downstream steps, demonstrably improves reranking performance on logical and mathematical reasoning tasks. Your PRMs will benefit from fine-grained supervision that explicitly models prefix validity and error propagation, enhancing first-error localization capabilities and overall model robustness.

Key insights

Synthesized, verifiable process data with controlled errors significantly improves PRM performance in reasoning tasks.

Principles

• Error injection should be template-aware.
• Recompute downstream steps under corrupted state.
• Verify injected step is non-derivable from prefix.

Method

The framework constructs a correct symbolic chain, injects a template-aware error, recomputes subsequent steps, verifies non-derivability, then translates paired chains into natural language.

In practice

• Train PRMs for improved reranking.
• Develop diagnostic benchmarks for first-error localization.
• Generate diverse, controlled error types.

Topics

• Process Reward Models
• Data Synthesis
• Error Injection
• Symbolic Reasoning
• Logical Reasoning
• Mathematical Reasoning

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.