Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912849598611456 |
|---|---|
| author | Guo, Zhenyuan Chen, Tong Meng, Wenlong Gong, Chen Yu, Xin Wei, Chengkun Chen, Wenzhi |
| author_facet | Guo, Zhenyuan Chen, Tong Meng, Wenlong Gong, Chen Yu, Xin Wei, Chengkun Chen, Wenzhi |
| contents | Large Reasoning Models (LRMs) excel at solving complex problems by explicitly generating a reasoning trace before deriving the final answer. However, these extended generations incur substantial memory footprint and computational overhead, bottlenecking LRMs' efficiency. This work uses attention maps to analyze the influence of reasoning traces and uncover an interesting phenomenon: only some decision-critical tokens in a reasoning trace steer the model toward the final answer, while the remaining tokens contribute negligibly. Building on this observation, we propose Dynamic Thinking-Token Selection (DynTS). This method identifies decision-critical tokens and retains only their associated Key-Value (KV) cache states during inference, evicting the remaining redundant entries to optimize efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18383 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models Guo, Zhenyuan Chen, Tong Meng, Wenlong Gong, Chen Yu, Xin Wei, Chengkun Chen, Wenzhi Artificial Intelligence Computation and Language Machine Learning Large Reasoning Models (LRMs) excel at solving complex problems by explicitly generating a reasoning trace before deriving the final answer. However, these extended generations incur substantial memory footprint and computational overhead, bottlenecking LRMs' efficiency. This work uses attention maps to analyze the influence of reasoning traces and uncover an interesting phenomenon: only some decision-critical tokens in a reasoning trace steer the model toward the final answer, while the remaining tokens contribute negligibly. Building on this observation, we propose Dynamic Thinking-Token Selection (DynTS). This method identifies decision-critical tokens and retains only their associated Key-Value (KV) cache states during inference, evicting the remaining redundant entries to optimize efficiency. |
| title | Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models |
| topic | Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2601.18383 |