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Ziang Ye

2 accepted papers

2026

Learning to Self-Verify Makes Language Models Better Reasoners

ICML 2026poster

Recent large language models (LLMs) achieve strong performance in generating promising reasoning paths for complex tasks. However, despite powerful generation ability, LLMs remain weak at verifying their own answers, revealing a persistent capability asymmetry between generation and self-verificatio…

Cited by 0SourceScholar
2025

Disentangling Reasoning Tokens and Boilerplate Tokens For Language Model Fine-tuning

ACL 2025finding

When using agent-task datasets to enhance agent capabilities for Large Language Models (LLMs), current methodologies often treat all tokens within a sample equally. However, we argue that tokens serving different roles—specifically, reasoning tokens versus boilerplate tokens (e.g., those governing o…

Cited by 0SourcePDFScholar