First Finish Search: Efficient Test-Time Scaling in Large Language
  Models
Test-time scaling (TTS), which involves dynamic allocation of compute during
inference, offers a promising way to improve reasoning in large language
models. While existing TTS methods work well, they often rely on long decoding
paths or require a large number of samples to be generated, increasing the
token usage and inference latency. We observe the surprising fact that for
reasoning tasks, shorter traces are much more likely to be correct than longer
ones. Motivated by this, we introduce First Finish Search (FFS), a
training-free parallel decoding strategy that launches n independent samples
and returns as soon as any one completes. We evaluate FFS alongside simple
decoding, beam search, majority voting, and budget forcing on four reasoning
models (DeepSeek-R1, R1-Distill-Qwen-32B, QwQ-32B and Phi-4-Reasoning-Plus) and
across four datasets (AIME24, AIME25-I, AIME25-II and GPQA Diamond). With
DeepSeek-R1, FFS achieves 82.23% accuracy on the AIME datasets, a 15%
improvement over DeepSeek-R1's standalone accuracy, nearly matching OpenAI's
o4-mini performance. Our theoretical analysis explains why stopping at the
shortest trace is likely to yield a correct answer and identifies the
conditions under which early stopping may be suboptimal. The elegance and
simplicity of FFS demonstrate that straightforward TTS strategies can perform
remarkably well, revealing the untapped potential of simple approaches at
inference time.
6837e51d05c81fd7d7d1962e
Aradhye Agarwal
claimed_verified
2025-05-29T07:41:47.337Z
63ca499104c97982831127ec
/avatars/816a962c62c805adf7789fa8e398b01e.svg
Aradhye Agarwal
user
aradhye
6837e51d05c81fd7d7d1962f
Ayan Sengupta
6837e51d05c81fd7d7d19630
Tanmoy Chakraborty
First Finish Search: Efficient Test-Time Scaling in Large Language
  Models
Test-time scaling (TTS), which involves dynamic allocation of compute during
inference, offers a promising way to improve reasoning in large language
models. While existing TTS methods work well, they often rely on long decoding
paths or require a large number of samples to be generated, increasing the
token usage and inference latency. We observe the surprising fact that for
reasoning tasks, shorter traces are much more likely to be correct than longer
ones. Motivated by this, we introduce First Finish Search (FFS), a
training-free parallel decoding strategy that launches n independent samples
and returns as soon as any one completes. We evaluate FFS alongside simple
decoding, beam search, majority voting, and budget forcing on four reasoning
models (DeepSeek-R1, R1-Distill-Qwen-32B, QwQ-32B and Phi-4-Reasoning-Plus) and
across four datasets (AIME24, AIME25-I, AIME25-II and GPQA Diamond). With
DeepSeek-R1, FFS achieves 82.23% accuracy on the AIME datasets, a 15%
improvement over DeepSeek-R1's standalone accuracy, nearly matching OpenAI's
o4-mini performance. Our theoretical analysis explains why stopping at the
shortest trace is likely to yield a correct answer and identifies the
conditions under which early stopping may be suboptimal. The elegance and
simplicity of FFS demonstrate that straightforward TTS strategies can perform
remarkably well, revealing the untapped potential of simple approaches at
inference time.
First Finish Search improves accuracy in large language models by stopping inference at the first completed sample, significantly outperforming other decoding strategies in reasoning tasks.
Test-time scaling TTS dynamic allocation inference reasoning tasks First Finish Search FFS parallel decoding decoding strategies beam search majority voting budget forcing accuracy performance inference latency early stopping