Fewer Steps, Better Answers: How to Develop Efficient Reasoning in LLMs — AI Innovations and Insights 46

Welcome back! Let’s dive into Chapter 46 of this delightful series!

Large language models (LLMs) shine at tackling complex tasks like math and coding through chain-of-thought (CoT) reasoning. But there’s a catch: the step-by-step explanations they generate can get unnecessarily long, leading to significant computational overhead — a problem often referred to as the "overthinking phenomenon."

Figure 1: An example of the “overthinking phenomenon”: when asked “Which is larger, 0.9 or 0.11?”, the reasoning model takes an unnecessarily long time (e.g., 19 seconds for QwQ-32B and 42 seconds for DeepSeek-R1) to arrive at the correct answer. This example was tested in March 2025. [Source].

As shown in Figure 1, when answering a simple question like "Which is bigger, 0.9 or 0.11?", a reasoning model might still produce hundreds of redundant reasoning tokens, driving up both inference time and cost.

That’s why studying efficient reasoning in LLMs matters more than ever.

Figure 2: The pipeline of developing efficient reasoning for LLMs. [Source].

As shown in Figure 2, reasoning LLMs are usually built on top of base models using supervised fine-tuning (SFT), reinforcement learning (RL), or a mix of both.

"Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models" explores how to reduce unnecessary reasoning steps without losing accuracy—and even how to fine-tune general models to reason more efficiently. The goal is to help models deliver clear, concise answers supported by just enough reasoning—not more than needed.

Figure 3: Overview of efficient reasoning methods, which can be summarized as model-oriented (Left) and reasoning output-oriented (Middle), and input prompts-oriented (Right) methods. [Source].

As shown in Figure 3, the existing research can be divided into three main approaches:

• Model-based methods focus on training techniques—like using reinforcement learning to reward shorter reasoning paths, or fine-tuning with chain-of-thought data of varying lengths.

• Output-based methods work by either compressing reasoning steps into a compact latent space or dynamically adjusting the reasoning process during inference.

• Prompt-based methods guide the model’s behavior through prompt design—for example, by setting a maximum reasoning length or routing queries based on their difficulty. The goal is to encourage more concise and efficient responses.

Figure 4 gives an overview of the related methods.

Figure 4: Taxonomy of existing literature on efficient reasoning for LLMs. [Source].

Thoughts and Insights

LLMs today are caught in a tough trade-off between reasoning quality and efficiency. As reasoning chains get longer, the cost keeps climbing—making it hard to deploy these models in real-world scenarios like search, dialogue systems, or autonomous driving. In many cases, inefficient reasoning has become a real bottleneck.

Here are a few practical tips I’ve found helpful in real-world use:

• Use a token budget in your prompts to keep responses focused and efficient.

• For long conversations or large corpus RAG tasks, try KV-Cache compression with INT8 or INT4—it can save a lot of memory.

• In high-load scenarios or when working with very long inputs, consider using Latent Chain of Thought (Latent CoT) to ease the processing burden.

Overall, improving reasoning efficiency isn’t just a technical challenge—it’s quickly becoming a key industry priority. Over the next few years, "efficient reasoning" is likely to emerge as one of the central themes in optimizing AI infrastructure.