Less is More: Improving LLM Reasoning with Minimal Test-Time
  Intervention
Recent progress in large language models (LLMs) has focused on test-time
scaling to improve reasoning via increased inference computation, but often at
the cost of efficiency. We revisit test-time behavior and uncover a simple yet
underexplored phenomenon: reasoning uncertainty is highly localized-only a
small subset of high-entropy tokens dominantly affects output correctness.
Motivated by this, we propose Minimal Test-Time Intervention (MTI), a
training-free framework that enhances reasoning accuracy and stability with
minimal overhead. MTI includes: (i) Selective CFG intervention, applying
classifier-free guidance only at uncertain positions; and (ii) Lightweight
negative-prompt guidance, reusing the main model's KV cache to approximate
unconditional decoding efficiently. MTI yields consistent gains across general,
coding, and STEM tasks-e.g., +1.35% average improvement on eight benchmarks for
Qwen3-8B-Base and +5% on AIME2024 using Qwen3-32B-Reasoning-while remaining
highly efficient.
68f191746e0bef323a68fbb5
Zhen Yang
claimed_verified
2025-10-18T04:49:56.134Z
63f58403fcf95ecac2b33d78
/avatars/a77ea80784896502ae1cfa086a78ce66.svg
Zhen Yang
user
YZCS
68f191746e0bef323a68fbb6
Mingyang Zhang
68f191746e0bef323a68fbb7
Feng Chen
68f191746e0bef323a68fbb8
Ganggui Ding
68f191746e0bef323a68fbb9
Liang Hou
68f191746e0bef323a68fbba
Xin Tao
68f191746e0bef323a68fbbb
Pengfei Wan
68f191746e0bef323a68fbbc
Ying-Cong Chen
Less is More: Improving LLM Reasoning with Minimal Test-Time
  Intervention
Recent progress in large language models (LLMs) has focused on test-time
scaling to improve reasoning via increased inference computation, but often at
the cost of efficiency. We revisit test-time behavior and uncover a simple yet
underexplored phenomenon: reasoning uncertainty is highly localized-only a
small subset of high-entropy tokens dominantly affects output correctness.
Motivated by this, we propose Minimal Test-Time Intervention (MTI), a
training-free framework that enhances reasoning accuracy and stability with
minimal overhead. MTI includes: (i) Selective CFG intervention, applying
classifier-free guidance only at uncertain positions; and (ii) Lightweight
negative-prompt guidance, reusing the main model's KV cache to approximate
unconditional decoding efficiently. MTI yields consistent gains across general,
coding, and STEM tasks-e.g., +1.35% average improvement on eight benchmarks for
Qwen3-8B-Base and +5% on AIME2024 using Qwen3-32B-Reasoning-while remaining
highly efficient.
Minimal Test-Time Intervention (MTI) enhances reasoning accuracy and stability in large language models with minimal overhead by selectively applying classifier-free guidance and lightweight negative-prompt guidance.
large language models test-time scaling reasoning uncertainty high-entropy tokens Minimal Test-Time Intervention Selective CFG intervention classifier-free guidance Lightweight negative-prompt guidance KV cache unconditional decoding Qwen3-8B-Base Qwen3-32B-Reasoning AIME2024