Building a Better LLM Reasoner
Published on: September 10, 2025
Tags: #llm #reasoning #ai
Large Language Models (LLMs) often feel like magic black boxes. We give them a prompt, and they give us an answer. But what happens when the reasoning is complex and the model gets it wrong?
Inspired by a recent talk on LLM Reasoning by Google DeepMind's Denny Zhou, I built a Python project, Cognition Synthesis, to explore a crucial idea: we can programmatically make LLMs better thinkers.
Here are the key insights from the talk and how I implemented them.
Insight 1: Force the LLM to Show Its Work
A model is more likely to arrive at a correct multi-step answer if it's forced to reason step-by-step. This is the core idea of Chain-of-Thought (CoT) prompting. Instead of just asking for the answer, we ask the model to "think step by step."
In Cognition Synthesis: This is the foundation. A PromptManager class constructs prompts that append the magic phrase, "Let's think step by step," encouraging the model to produce a reasoning path instead of just a final answer.
Insight 2: Trust the Crowd, Not the Individual
A single LLM generation can be flawed. However, if you generate multiple, diverse reasoning paths, the most frequent answer is remarkably accurate. This technique, called Self-Consistency, treats each generation like a vote in an election.
In Cognition Synthesis: The SelfConsistency class implements this. For a single problem, it calls the LLM 5-8 times with a non-zero temperature to generate varied responses. It then uses a robust AnswerParser to extract the final answer from each one and returns the answer that received the most "votes." This simple idea dramatically improves reliability.
Insight 3: Create a Self-Improving Flywheel
Denny Zhou highlighted that the best data for teaching an AI to reason often comes from the AI itself—as long as it's verified. This is the core of techniques like Reinforcement Learning from AI Feedback (RLAIF). You let the model generate many solutions, use a verifier to pick the correct ones, and use that filtered data to train a better model.
In Cognition Synthesis: The DataGenerator pipeline simulates this flywheel.
1. It takes a problem with a known correct answer from a ProblemBank.
2. It uses SelfConsistency to generate multiple reasoning paths.
3. A Verifier checks each path's answer against the ground truth.
4. Only the correct (problem, reasoning_path) pairs are saved to a training_data.jsonl file.
This final file is a high-quality, AI-generated dataset, ready to be used for fine-tuning.
The Next Frontier: Reasoning Beyond Verifiable Answers
The biggest challenge ahead, as Zhou noted, lies in tasks that don't have a single, verifiable answer. How do we measure the quality of creative writing? Or the elegance of a software design? Developing verifiers and reward models for these subjective, complex domains is the next great frontier in LLM reasoning.
Conclusion
The journey from a simple prompt to a self-improving data pipeline shows that the future of reliable AI isn't just about bigger models. It's about smarter, programmatic techniques to guide and validate their reasoning process.
To see these concepts in action, check out the implementation on GitHub: https://github.com/zhu-weijie/cognition-synthesis
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