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About this paper
Chuhuai Yue, Chengqi Dong, Yinan Gao, Hang He, Jiajun Chai, Guojun Yin, Wei Lin
Researchers from Meituan and affiliated universities introduced the Verifiable Stepwise Reward Mechanism (VSRM) to mitigate 'overthinking' in Large Reasoning Models (LRMs) by rewarding effective intermediate steps and penalizing ineffective ones. VSRM significantly reduced output length by up to 5540 tokens while maintaining or slightly improving reasoning accuracy across six mathematical benchmarks, enabling more efficient LRM deployment.
Key insights
- The core of the 'overthinking' problem in LRMs stems from generating ineffective intermediate steps rather than merely excessive output length.
- Providing verifiable, stepwise rewards based on intermediate accuracy changes offers a more fundamental and robust solution compared to length-based penalties or unstable Process Reward Models (PRMs).
- A propagation decay mechanism is crucial for generating denser reward signals, enabling more effective learning in reinforcement learning settings when immediate accuracy changes are sparse.
Summary by alphaXiv.
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