title: First Finish Search: Efficient Test-Time Scaling in Large Language
    Models
summary: 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.
paper_authors._id: 6837e51d05c81fd7d7d1962e
paper_authors.name: Aradhye Agarwal
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:41:47.337Z
paper_authors._id: 63ca499104c97982831127ec
paper_authors.avatarUrl: /avatars/816a962c62c805adf7789fa8e398b01e.svg
paper_authors.fullname: Aradhye Agarwal
paper_authors.type: user
paper_authors.user: aradhye
paper_authors._id: 6837e51d05c81fd7d7d1962f
paper_authors.name: Ayan Sengupta
paper_authors._id: 6837e51d05c81fd7d7d19630
paper_authors.name: Tanmoy Chakraborty
paper_title: First Finish Search: Efficient Test-Time Scaling in Large Language
    Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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