mlsanityData to 7 Oct 2026

Flaming-hot Initiation with Regular Execution Sampling for Large Language Models

arXiv 2410.21236 ↗First posted 28 Oct 2024cs.LG cs.AI cs.CLcited by 6

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Abstract (arXiv)

Since the release of ChatGPT, large language models (LLMs) have demonstrated remarkable capabilities across various domains. A key challenge in developing these general capabilities is efficiently sourcing diverse, high-quality data. This becomes especially critical in reasoning-related tasks with sandbox checkers, such as math or code, where the goal is to generate correct solutions to specific problems with higher probability. In this work, we introduce Flaming-hot Initiation with Regular Execution (FIRE) sampling, a simple yet highly effective method to efficiently find good responses. Our empirical findings show that FIRE sampling enhances inference-time generation quality and also benefits training in the alignment stage. Furthermore, we explore how FIRE sampling improves performance by promoting diversity and analyze the impact of employing FIRE at different positions within a response.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 31 of its 47 references that are in the map. First posted on arXiv 28 Oct 2024.

Builds on

31 of 47 references.

References · 31

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19 more
  1. 2023Code Llama: Open Foundation Models for Codecited by 2,105
  2. 2024Qwen2 Technical Reportcited by 2,076
  3. 2024HybridFlow: A Flexible and Efficient RLHF Frameworkcited by 1,980
  4. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,928
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 2.

6 papers.

  1. 2025Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Modelscited by 35
  2. 2025LSPO: Length-aware Dynamic Sampling for Policy Optimization in LLM Reasoningcited by 9
  3. 2025Iterative Deepening Sampling as Efficient Test-Time Scalingcited by 8
  4. 2025Think Outside the Policy: In-Context Steered Policy Optimizationcited by 3
  5. 2026Partial Reasoning in Language Models: Search and Refinement Guided by Uncertaintycited by 0
  6. 2026Large Language Models Explore by Latent Distillingcited by 0