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

ACL ARR 2025 February Submission7574 Authors

Abstract: 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 output format)—differ significantly in importance and learning complexity, necessitating their disentanglement and distinct treatment. To address this, we propose a novel Shuffle-Aware Discriminator (SHAD) for adaptive token discrimination. SHAD classifies tokens by exploiting predictability differences observed after shuffling input-output combinations across samples: boilerplate tokens, due to their repetitive nature among samples, maintain predictability, whereas reasoning tokens do not. Using SHAD, we propose the Reasoning-highlighted Fine-Tuning (RFT) method, which adaptively emphasizes reasoning tokens during fine-tuning, yielding notable performance gains over common Supervised Fine-Tuning (SFT).

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: Large language models, Tool Use, Agent, Reasoning

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data analysis

Languages Studied: english

Submission Number: 7574