arXiv AI

Supervised Fine-Tuning for Reasoning Large Language Models: A Survey

The paper surveys recent progress in supervised fine‑tuning (SFT) for large language models that excel at reasoning tasks. It reviews methodological advances, grouping them into data‑centric and algorithm‑centric approaches, and reorganizes analytical studies by data characteristics, optimization dynamics, and mechanistic insights. The survey also identifies current research focuses, remaining bottlenecks, and suggests future directions for reasoning SFT.

arXiv AI

Sep 7

Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models

The paper surveys efficient reasoning in large language models, contrasting fast intuitive (System 1) and slow deep (System 2) reasoning. It analyzes why System 2 is computationally costly yet more accurate, and why System 1 is efficient but less effective. The survey covers causes of inefficiency, patterns of reasoning behavior, and potential solutions to balance performance and computational budgets, offering actionable insights and an open‑source repository for ongoing research.

By Rui Wang, Hongru Wang, Boyang Xue, Jianhui Pang, Shudong Liu, Yi Chen, Jiahao Qiu, Derek Fai Wong, Heng Ji, Kam-Fai Wong

llms

arXiv AI

Jul 8

Mathematical Reasoning in Large Language Models: Benchmarks, Architectures, Evaluation, and Open Challenges

arXiv:2605. 19723v2 Announce Type: replace-cross Abstract: Mathematical reasoning is essential for problem-solving in education, science, and industry, serving as a crucial benchmark for evaluating artificial intelligence systems.

By Husnain Amjad, Raja Khurram Shahzad, Aamir Shahzad, Mehwish Fatima

llms fine-tuning benchmarks

arXiv AI

Oct 21

CHIMERA: Compact Synthetic Data for Generalizable LLM Reasoning

arXiv:2603.00889v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, largely enabled by supervised fine-tuning (SFT)- and reinforce...

By Xinyu Zhu, Yihao Feng, Yanchao Sun, Xianzhi Du, Pingzhi Li, Olli Saarikivi, Yun Zhu, Yu Meng

llms reinforcement-learning fine-tuning benchmarks

arXiv Computation and Language

Sep 10

Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning

arXiv:2609.10445v1 Announce Type: new Abstract: Reasoning language models have made substantial advances on a variety of complex tasks, yet their capabilities remain overwhelmingly English-centric: m...

By Mehrnaz Mofakhami, Ananya Sahu, Alejandro R. Salamanca, Daniel D'souza, Alexandre Berard, Thomas Euyang, Marzieh Fadaee, Julia Kreutzer

llms fine-tuning benchmarks

arXiv AI

Jun 4

Longer Context, Deeper Thinking: Uncovering the Role of Long-Context Ability in Reasoning

arXiv:2505. 17315v2 Announce Type: replace Abstract: Recent language models exhibit strong reasoning capabilities, yet the influence of long-context capacity on reasoning remains underexplored.

By Wang Yang, Zirui Liu, Hongye Jin, Qingyu Yin, Vipin Chaudhary, Xiaotian Han

llms fine-tuning benchmarks

arXiv AI

Jun 15

Implicit Reasoning for Large Language Model-based Generative Recommendation

arXiv:2606. 14142v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge.

By Yinhan He, Liam Collins, Bhuvesh Kumar, Jundong Li, Neil Shah, Donald Loveland

llms rag safety

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).

By Hongyi Henry Jin, Wenhan Yang, Meysam Ghaffari, Carlos Morato, Baharan Mirzasoleiman

llms fine-tuning data-engineering

arXiv AI

Jul 7

Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs

arXiv:2602. 16512v2 Announce Type: replace Abstract: Prompting schemes such as Chain of Thought, Tree of Thoughts, and Graph of Thoughts can significantly enhance the reasoning capabilities of large language models.

By Felix Fricke, Simon Malberg, Georg Groh

llms

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.

llms fine-tuning data-engineering

arXiv AI

Jul 17

Step-Tagging: Toward controlling the generation of Language Reasoning Models through step monitoring

arXiv:2512. 14332v2 Announce Type: replace-cross Abstract: The field of Language Reasoning Models (LRMs) has been very active over the past few years with advances in training and inference techniques enabling LRMs to reason longer, and more accurately.

By Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo, John D. Kelleher

benchmarks

arXiv Computation and Language

Sep 30

Layer-Informed Fine-Tuning via Three-Stage Functional Segmentation of LLMs

The paper proposes Layer-Informed Fine-Tuning (LIFT), a method that identifies and updates only the most functionally critical layers of large language models (LLMs) using a bottleneck identification mechanism based on sensitivity analysis. By focusing on layers that handle conceptualization, reasoning, and textualization, LIFT aims to accelerate training and enhance performance on reasoning tasks. Experiments demonstrate that this selective fine-tuning approach both speeds up the training process and yields significant performance gains.

By Junning Shao, Siwei Wang, Zhixuan Fang

llms computer-vision fine-tuning benchmarks

arXiv Computation and Language

Sep 1

Reasoning Beyond Language: A Comprehensive Survey on Latent Chain-of-Thought Reasoning

arXiv:2505.16782v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT...

By Xinghao Chen, Anhao Zhao, Heming Xia, Xuan Lu, Hanlin Wang, Yanjun Chen, Wei Zhang, Jian Wang, Wenjie Li, Xiaoyu Shen

llms nlp

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