Rewarding Structural Conformance of Reasoning using Process Mining
📰 ArXiv cs.AI
Learn how to apply process mining to improve reinforcement learning for reasoning tasks by rewarding structural conformance, enhancing feedback on intermediate steps
Action Steps
- Apply process mining to analyze the structural conformance of reasoning steps
- Use sparse reward policy gradient methods to enable effective reinforcement learning
- Implement a reward function that evaluates the overall reasoning quality
- Test the approach on mathematical problem solving tasks to evaluate its effectiveness
- Compare the results with traditional reinforcement learning methods to assess the improvement
Who Needs to Know This
Researchers and developers working on reinforcement learning and language models can benefit from this approach to improve the quality of reasoning tasks, such as mathematical problem solving
Key Insight
💡 Process mining can enhance reinforcement learning by providing more informative feedback on intermediate reasoning steps
Share This
🤖 Improve reinforcement learning for reasoning tasks with process mining! 📈
Key Takeaways
Learn how to apply process mining to improve reinforcement learning for reasoning tasks by rewarding structural conformance, enhancing feedback on intermediate steps
Full Article
Title: Rewarding Structural Conformance of Reasoning using Process Mining
Abstract:
arXiv:2510.25065v3 Announce Type: replace Abstract: Recent advances in sparse reward policy gradient methods have enabled effective reinforcement learning (RL)-based language model post-training. However, for reasoning tasks such as mathematical problem solving, binarized outcome rewards provide limited feedback on intermediate reasoning steps. While some studies have attempted to address this issue by estimating overall reasoning quality, it remains unclear whether these rewards are reliable pr
Abstract:
arXiv:2510.25065v3 Announce Type: replace Abstract: Recent advances in sparse reward policy gradient methods have enabled effective reinforcement learning (RL)-based language model post-training. However, for reasoning tasks such as mathematical problem solving, binarized outcome rewards provide limited feedback on intermediate reasoning steps. While some studies have attempted to address this issue by estimating overall reasoning quality, it remains unclear whether these rewards are reliable pr
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