Making LLMs think better: Three practical ways to improve reasoning accuracy

When LLMs sound smart but reason wrong

Large language models (LLMs) are remarkably good at explaining concepts, writing code, and summarizing information. But when asked to solve problems that require multi-step reasoning, things can break down.

Consider this: A model may clearly explain how tax deductions work but still miscalculate a multi-step tax liability scenario. Similarly, a chatbot might correctly describe loan eligibility rules but produce the wrong EMI when multiple conditions are applied in sequence.

The underlying challenge is that LLMs are pattern predictors rather than true reasoning engines. They are trained to predict the most likely next token based on patterns in the data. As a result, they can struggle with tasks that require step-by-step logical processing.

Typical failure cases include:

• multi-step logical problems
• numerical reasoning
• decisions that depend on multiple intermediate steps.

Typical solutions to improve reasoning accuracy usually involve model retraining, domain fine-tuning, or building new datasets. While these approaches can work, they are often expensive, slow to implement, and difficult to deploy in production environments.

An alternative is to improve reasoning during inference - the stage where the model generates responses. In our evaluation, we explored three inference-time techniques designed to improve reasoning reliability:

• Self-Consistency
• Dual-Model Cross Verification
• Self-Reflection

These techniques were evaluated using logical reasoning tasks and Chain-of-Thought prompting, which encourages the model to show intermediate reasoning steps before producing a final answer.

The experimental setup

To evaluate these approaches, we used the Logical Reasoning Improvement dataset from Hugging Face, originally released under the Open-Platypus project.

All experiments relied on Chain-of-Thought prompting, where the model is encouraged to:

1. Analyze the problem
2. Break the reasoning into steps
3. Produce the final answer.

The objective was straightforward: improve reasoning accuracy without retraining the model, using only inference-time strategies.

Self-consistency: Generating multiple reasoning paths

The first technique focuses on generating multiple reasoning attempts instead of relying on a single model response.

Rather than asking the model a question once, the same prompt is executed several times with controlled randomness. Each run produces a slightly different reasoning path. The system then selects the most frequent final answer using majority voting.

For example:

A store offers a 20% discount on a ₹1000 item and then adds 5% tax. What is the final price?

A single model response might incorrectly apply the steps. With self-consistency, however, the model generates several reasoning paths. By comparing the final answers, the system chooses the result that appears most often.

In our evaluation with the MLM model, this technique resulted in a +9% to +15% improvement in reasoning accuracy, using temperature = 0.8 and nucleus sampling (top-p = 0.9) during inference. These improvements are specific to this model setup and task configuration.

This approach is especially useful in domains where accuracy improvements are valuable but occasional mistakes are manageable, such as:

• educational tools
• knowledge assistants
• customer support automation
• general information retrieval.

Dual-model cross verification

Another approach is to use two independent models to solve the same reasoning problem and compare their outputs.

The process typically works as follows:

1. Model A generates an answer with reasoning.
2. Model B independently solves the same problem.
3. The outputs are compared.

If both models arrive at the same answer, the response can be accepted with greater confidence. If the answers differ, the system can flag the case for further review.

This technique works because reasoning errors across architecturally different LLMs are often not strongly correlated. When two independent models converge on the same result, the likelihood of correctness increases.

This matters in domains such as insurance and finance, where the cost of a wrong decision can be very high.

However, the technique also introduces trade-offs:

• higher compute requirements
• increased response latency.

As a result, it is often best suited for moderate-risk decision workflows where additional validation is valuable.

Self-reflection: Reviewing the model’s own reasoning

The third technique asks the model to review and critique its own reasoning before producing a final answer.

The workflow typically involves three steps:

1. Generate an initial solution with reasoning.
2. Ask the model to review the reasoning and identify possible errors.
3. Produce a revised answer.

In theory, this allows the model to identify flaws in its own logic and correct them.

In our evaluation with the MLM model, however, self-reflection resulted in only minor improvements in reasoning accuracy. The effectiveness of this technique depends heavily on the model’s inherent reasoning capability and scale.

Because of this limitation, self-reflection may be more suitable for internal tools or educational scenarios, where transparency into reasoning steps is useful.

Practical ways to apply these techniques

Different applications may benefit from different approaches.

For general AI assistants, self-consistency offers a practical way to improve reasoning accuracy with relatively low implementation complexity.

For decision-support environments where accuracy matters more, dual-model cross verification can introduce an additional validation layer before responses are used.

These approaches show that meaningful improvements in reasoning reliability can often be achieved without retraining models, simply by designing smarter inference workflows.

Conclusion: Moving toward more reliable AI systems

LLMs are increasingly moving from text generation tools to decision-support systems. As this shift happens, improving reasoning reliability becomes essential.

Inference-time techniques offer several advantages:

• immediate improvements
• minimal engineering overhead
• no need for retraining large models.

In many cases, better reasoning comes not from building bigger models, but from designing smarter ways to use the models we already have.