PATS: Process-Level Adaptive Thinking Mode Switching
Current large-language models (LLMs) typically adopt a fixed reasoning
strategy, either simple or complex, for all questions, regardless of their
difficulty. This neglect of variation in task and reasoning process complexity
leads to an imbalance between performance and efficiency. Existing methods
attempt to implement training-free fast-slow thinking system switching to
handle problems of varying difficulty, but are limited by coarse-grained
solution-level strategy adjustments. To address this issue, we propose a novel
reasoning paradigm: Process-Level Adaptive Thinking Mode Switching (PATS),
which enables LLMs to dynamically adjust their reasoning strategy based on the
difficulty of each step, optimizing the balance between accuracy and
computational efficiency. Our approach integrates Process Reward Models (PRMs)
with Beam Search, incorporating progressive mode switching and bad-step penalty
mechanisms. Experiments on diverse mathematical benchmarks demonstrate that our
methodology achieves high accuracy while maintaining moderate token usage. This
study emphasizes the significance of process-level, difficulty-aware reasoning
strategy adaptation, offering valuable insights into efficient inference for
LLMs.
68355ce06a9c239ada09f97b
Yi Wang
claimed_verified
2025-05-27T07:49:06.169Z
66e0404662d6ab4f1107580f
/avatars/ef71694fea5482078a637a3869e30d19.svg
Yi Wang
user
Yi53
68355ce06a9c239ada09f97c
Junxiao Liu
claimed_verified
2025-05-27T07:55:01.261Z
68356f5db243fb809813a715
https://cdn-avatars.huggingface.co/v1/production/uploads/68356f5db243fb809813a715/grhHvANfDRp75rMJxWlQo.jpeg
LiuJunxiao
user
master-lan
68355ce06a9c239ada09f97d
Shimao Zhang
admin_assigned
2025-05-27T12:34:46.475Z
65080dc63fc966d1bbba485d
/avatars/347890233f2316e7f7a04d652b2378bb.svg
Shimao Zhang
user
Shimao-Zhang
68355ce06a9c239ada09f97e
Jiajun Chen
68355ce06a9c239ada09f97f
Shujian Huang
PATS: Process-Level Adaptive Thinking Mode Switching
Current large-language models (LLMs) typically adopt a fixed reasoning
strategy, either simple or complex, for all questions, regardless of their
difficulty. This neglect of variation in task and reasoning process complexity
leads to an imbalance between performance and efficiency. Existing methods
attempt to implement training-free fast-slow thinking system switching to
handle problems of varying difficulty, but are limited by coarse-grained
solution-level strategy adjustments. To address this issue, we propose a novel
reasoning paradigm: Process-Level Adaptive Thinking Mode Switching (PATS),
which enables LLMs to dynamically adjust their reasoning strategy based on the
difficulty of each step, optimizing the balance between accuracy and
computational efficiency. Our approach integrates Process Reward Models (PRMs)
with Beam Search, incorporating progressive mode switching and bad-step penalty
mechanisms. Experiments on diverse mathematical benchmarks demonstrate that our
methodology achieves high accuracy while maintaining moderate token usage. This
study emphasizes the significance of process-level, difficulty-aware reasoning
strategy adaptation, offering valuable insights into efficient inference for
LLMs.
PATS enhances LLM efficiency by dynamically adjusting reasoning strategies based on task difficulty, leveraging PRMs and Beam Search.
large-language models (LLMs) reasoning strategy task and reasoning process complexity training-free fast-slow thinking system switching Process-Level Adaptive Thinking Mode Switching (PATS) Process Reward Models (PRMs) Beam Search progressive mode switching bad-step penalty mechanisms mathematical benchmarks process-level difficulty-aware reasoning strategy adaptation