OpenAI Blog
Sep 12, 2024
OpenAI o1-mini
Advancing cost-efficient reasoning
Apriel-H1: The Surprising Key to Distilling Efficient Reasoning Models
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arXiv AI
Jun 8
Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces
arXiv:2606. 06840v1 Announce Type: cross Abstract: Modern reasoning models offer surprisingly strong zero-shot performance on challenging multi-label tasks that require selecting a small set of relevant options from hundreds of thousands to millions of candidate labels.
Hugging Face Blog
Jul 10, 2025
Kimina-Prover: Applying Test-time RL Search on Large Formal Reasoning Models
arXiv Machine Learning
Jun 26
Reasoning Quality Emerges Early: Data Curation for Reasoning Models
arXiv:2606. 26797v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) on a small, high-quality set of long reasoning traces is an effective approach for eliciting strong reasoning capabilities in Large Language Models (LLMs).
arXiv AI
Jun 30
Beyond Scaling Law: A Data-Efficient Distillation Framework for Reasoning
arXiv:2508. 09883v2 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate remarkable reasoning capabilities in tasks such as algorithmic coding and mathematical problem-solving.
arXiv AI
Jul 8
Base Models Know How to Reason, Thinking Models Learn When
arXiv:2510. 07364v4 Announce Type: replace Abstract: What do thinking language models learn during training that their base models lack?
Hugging Face Trending Papers
Jun 25
Reasoning Quality Emerges Early: Data Curation for Reasoning Models
Supervised fine-tuning (SFT) on a small, high-quality set of long reasoning traces is an effective approach for eliciting strong reasoning capabilities in Large Language Models (LLMs). However, existing methods for curating high-quality SFT data rely heavily on strong reasoning models to filter examples based on diversity and difficulty, making the curation process costly while often yielding suboptimal data quality.
arXiv AI
Jun 4
FALSIFYBENCH: Evaluating Inductive Reasoning in LLMs with Rule Discovery Games
arXiv:2606. 04751v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents in scientific tasks.
arXiv AI
Jun 8
DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling
arXiv:2606. 07108v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models (LRMs) demonstrate remarkable performance improvements by iteratively reflecting, exploring, and executing complex tasks, yet suffer from inefficiencies due to redundant reasoning, known as "overthinking".
arXiv AI
Jun 8
A Comprehensive Anatomy of Human and DeepSeek-R1 LLM Mathematical Reasoning
arXiv:2606. 07410v1 Announce Type: cross Abstract: The emergence of "Aha moments" in large language models, particularly DeepSeek-R1-0120, has raised the question of whether these systems genuinely reason or merely imitate the appearance of reasoning.
arXiv AI
Jun 4
Speculative Thinking: Enhancing Small-Model Reasoning with Large Model Guidance at Inference Time
arXiv:2504. 12329v2 Announce Type: replace-cross Abstract: Recent advances leverage post-training to enhance model reasoning performance, which typically requires costly training pipelines and still suffers from inefficient, overly lengthy outputs.
arXiv AI
Jun 6
LoRi: Low-Rank Distillation for Implicit Reasoning
arXiv:2606. 05315v1 Announce Type: cross Abstract: Implicit chain-of-thought (iCoT) methods aim to internalize reasoning in large language models, but often underperform explicit CoT prompting.