Learning to Generate Formally Verifiable Step-by-Step Logic Reasoning via Structured Formal Intermediaries
Summary: arXiv:2603.29500v1 Announce Type: new
Abstract: Large language models (LLMs) have recently demonstrated impressive performance on complex, multi-step reasoning tasks, especially when post-trained with outcome-rewarded reinforcement learning Guo et al. 2025. However, it has been observed that outcome rewards often overlook flawed intermediate steps, leading to unreliable reasoning steps even when final answers are correct. To address this unreliable reasoning, we propose PRoSFI (Process Reward over Structured Formal Intermediates), a novel reward method that enhances reasoning reliability without compromising accuracy.
Introduction
In recent years, advancements in artificial intelligence have led to the development of large language models (LLMs) that excel in various reasoning tasks. Despite their capabilities, these models face challenges when it comes to ensuring the reliability of their reasoning processes. Traditional methods of reinforcement learning often focus solely on final outcomes, neglecting the importance of intermediate reasoning steps. This article discusses a new approach to enhancing reasoning reliability in LLMs, known as PRoSFI.
Overview of PRoSFI
PRoSFI, or Process Reward over Structured Formal Intermediates, introduces a structured methodology for training LLMs. Instead of attempting to generate complete formal proofs, PRoSFI emphasizes the generation of structured intermediate steps that reflect the model’s natural language reasoning abilities. The key components of this approach include:
- Structured Intermediate Steps: The model generates logical steps that are aligned with human reasoning.
- Formal Verification: Each generated step is verified by a formal prover to ensure its accuracy.
- Reward System: Only fully validated reasoning chains receive high rewards, promoting the generation of reliable and accurate outputs.
Benefits of PRoSFI
Implementing PRoSFI offers several advantages over traditional reinforcement learning methods:
- Increased Reliability: By focusing on intermediate steps, PRoSFI helps identify flaws in reasoning that might otherwise go unnoticed.
- Enhanced Credibility: The incorporation of formal verification lends greater credibility to the final answers provided by the model.
- Improved Training Efficiency: The structured approach allows for more efficient training, as the model can learn from both successful and flawed reasoning paths.
Conclusion
As artificial intelligence continues to evolve, ensuring the reliability of reasoning in LLMs becomes increasingly important. PRoSFI presents a promising solution by integrating structured formal intermediates into the training process. By validating each step of reasoning, this method not only enhances the accuracy of final outputs but also fosters a deeper understanding of logical processes within AI systems. Future work will focus on refining this approach and exploring its applications across various domains.
Further Research
Continued research in this area will explore:
- The scalability of PRoSFI to larger models.
- Applications of this methodology in real-world reasoning tasks.
- Potential improvements in formal verification techniques.