Three Studies Examine Reasoning Capabilities and Training Methods for Large Language Models

Three Studies Examine Reasoning Capabilities and Training Methods for Large Language Models

Three recent papers on arXiv explore different aspects of large language model (LLM) development and capabilities.

Korean Language Reasoning

According to arXiv paper 2601.05459v1, researchers investigated whether LLMs require inherent reasoning abilities before applying reinforcement learning, specifically examining self-correction capabilities in Korean. The study notes that “Large Language Models (LLMs) demonstrate strong reasoning and self-correction abilities in high-resource languages like English, but their performance remains limited in low-resource languages such as Korean.”

Mathematical Reasoning After Fine-Tuning

A second study (arXiv:2504.11741v2) examined the impact of supervised fine-tuning (SFT) on mathematical reasoning. According to the paper, “Recent supervised fine-tuning (SFT) approaches have significantly improved language models’ performance on mathematical reasoning tasks, even when models are trained at a small scale.” The researchers investigated which specific capabilities are enhanced through fine-tuning.

Accelerating Reinforcement Learning

The third paper (arXiv:2509.23232v2) introduced SPEC-RL, a method to accelerate on-policy reinforcement learning. The study addresses how “Large Language Models (LLMs) increasingly rely on reinforcement learning with verifiable rewards (RLVR) to elicit reliable chain-of-thought reasoning,” while noting that current training processes remain “bottlenecked by the computationally expensive rollout.”