Improving Reasoning in LLMs
Large Language Models (LLMs) have evolved significantly from their early days of simplistic text generation. While generating fluent text was a remarkable achievement, the next frontier is enhancing their reasoning capabilities. True intelligence in AI is not just about predicting the next word but involves complex tasks such as solving math problems, debugging code, and drawing logical conclusions. The exciting question is: how are modern LLMs getting better at reasoning?
The answer lies in a suite of innovative techniques that enhance LLMs’ capacity for logical thought. Techniques like prompt engineering and agentic tool use are transforming these models into more methodical thinkers. Let’s explore five key strategies that are pushing the boundaries of reasoning in LLMs.
Chain Thought
Chain-of – Thought Prompting Technique. One of the most effective techniques for enhancing reasoning in LLMs is Chain-of – Thought (CoT) prompting. This method encourages models to articulate their thought processes before arriving at a final answer. For instance, instead of directly asking “What’s 17 times 24?”, you prompt the model with “Let’s think step by step.” This guidance leads the model to break down the multiplication into manageable components, such as 17 × 24 = (20 × 17) + (4 × 17).
Formalized in a 2022 study, CoT prompting has since been foundational for models like OpenAI’s o1, which was designed to “think longer before answering.” Its successor, o3, escalates this concept by incorporating simulated reasoning, allowing the model to pause mid-inference to reflect and refine its responses. This structured approach enables models to avoid hasty conclusions and manage complex multi-step logic effectively. The performance boost from these techniques is quantifiable; models employing CoT methods have shown a marked improvement in solving logical problems, with accuracy rates increasing by as much as 30% in specific benchmarks.
Inference Time
Inference-Time Compute Scaling Strategy. Another pivotal strategy for enhancing reasoning capabilities in LLMs is inference-time compute scaling. This technique allows models to allocate more computational resources during the generation phase, effectively enabling them to think longer and harder about complex questions. Instead of producing a single output, models can generate multiple reasoning paths, evaluate them, and select the most accurate one, a method known as “self-consistency.”
OpenAI’s o3-mini, for example, offers three reasoning effort options—low, medium, and high. At the high reasoning level, o3-mini has been shown to outperform even the full o1 model on various math and coding challenges. In fact, during testing, the model achieved a 15% improvement in accuracy over its predecessor when faced with difficult tasks. This approach, coupled with budget forcing techniques described in a 2025 paper, enables models to self-verify and correct errors, significantly enhancing their reasoning performance.
Reinforcement Learning
Reinforcement Learning and Multi-Stage Training. Reinforcement learning (RL) represents another groundbreaking method for improving LLM reasoning. Rather than merely forecasting the next word, models are trained to receive rewards for logical reasoning. OpenAI’s o1 and DeepSeek-R1 have utilized RL to cultivate sound reasoning patterns by rewarding correct multi-step answers. DeepSeek-R1’s initial iteration relied solely on RL, leading to inconsistencies in language. The final model employed a multi-stage training approach, combining RL for reasoning and supervised fine-tuning for clarity. This dual approach resulted in models that not only produce correct answers but also understand why those answers are correct, significantly enhancing their reliability. For instance, models trained with this method have demonstrated a 20% increase in problem-solving accuracy across various reasoning tasks compared to traditional training methods.
Self Correction
Self-Correction and Backtracking Mechanism. Self-correction has historically been a challenge for LLMs, but recent advancements have introduced backtracking methods to address this issue. Research has shown that simply asking a model to “try again” does not significantly improve answers; rather, it can sometimes exacerbate errors. In 2023, researchers discovered an “underthinking” issue where models would jump between ideas without maintaining a consistent line of reasoning. By 2025, innovations such as penalizing thought-switching and encouraging deeper exploration of ideas began to emerge. Techniques like self-backtracking allow models to rewind their reasoning when they encounter obstacles, exploring alternative paths. This method has led to accuracy improvements exceeding 40% compared to models relying solely on optimal reasoning solutions. These advancements effectively integrate search and planning capabilities into LLMs, enhancing their reasoning robustness.
Tool Use and External Knowledge Integration
Modern LLMs have started to incorporate external tools, broadening their reasoning capabilities. By invoking calculators, APIs, or web searches, LLMs can tackle complex queries that exceed their internal processing abilities. For example, Alibaba’s QwQ-32B can access APIs during its inference process, while Google’s Gemini 2.0 allows for code execution. This integration of tools matters significantly because it enables LLMs to handle tasks like verifying real-time data or performing complex calculations, which are beyond their intrinsic capabilities. By offloading these subtasks, LLMs can focus on higher-order reasoning, leading to better accuracy and reliability. The performance metrics in environments where these tools are utilized often show improvements of 25% or more in accuracy when compared to models that operate in isolation.
Conclusion on LLMs and Reasoning
The journey of LLMs toward improved reasoning is not a singular leap but rather a layered evolution of techniques that enhance their capabilities. From Chain-of – Thought prompting that adds structure to inference-time scaling that deepens reasoning processes, each strategy contributes to a more nuanced understanding of tasks. Reinforcement learning aligns models with logical reasoning, while backtracking enhances self-awareness. Finally, the ability to utilize external tools expands their reach and effectiveness. As we look to the future, expect tighter integration between internal reasoning processes and external decision-making tools. The leading models, such as OpenAI’s o1 and o3, DeepSeek’s R1, Google’s Gemini 2.0, and Alibaba’s QwQ, exemplify this hybrid approach, merging clever engineering with cognitive scaffolding. We are moving closer to LLMs that do not merely guess the next word but genuinely engage in thoughtful reasoning.
