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One-Token Verification for Reasoning Correctness Estimation
arXiv – CS AI|Zhan Zhuang, Xiequn Wang, Zebin Chen, Feiyang Ye, Ying Wei, Kede Ma, Yu Zhang||6 views
🤖AI Summary
Researchers introduce One-Token Verification (OTV), a new method that estimates reasoning correctness in large language models during a single forward pass, reducing computational overhead. OTV reduces token usage by up to 90% through early termination while improving accuracy on mathematical reasoning tasks compared to existing verification methods.
Key Takeaways
- →One-Token Verification (OTV) can assess reasoning correctness in LLMs without requiring multiple inference passes.
- →The method reduces token usage by up to 90% through correctness-guided early termination of generation.
- →OTV integrates into existing LLMs via low-rank adaptation without disrupting primary reasoning processes.
- →Experiments show OTV consistently outperforms existing verification methods on mathematical reasoning benchmarks.
- →The approach addresses key challenges of inference latency and reliability in multi-sample decoding strategies.
#llm#reasoning#verification#efficiency#mathematical-reasoning#token-optimization#inference-speed#machine-learning
Read Original →via arXiv – CS AI
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