Mind the Gap: How AI Researchers Are Fixing 'Thought Leaps' in Chain-of-Thought Reasoning
Large language models (LLMs) have made impressive strides in mathematical reasoning by using Chain-of-Thought (CoT) techniques—where models generate step-by-step explanations to solve problems. But there's a catch: these reasoning chains often suffer from 'Thought Leaps,' where experts skip intermediate steps, leaving gaps that confuse both AI models and human learners. A new paper from researchers at Zhejiang University, CUHK, and Microsoft Research Asia introduces CoT-Bridge, a method to detect and fill these gaps automatically, improving reasoning accuracy by up to +5.87% on benchmarks.
The Problem: Thought Leaps in AI Reasoning
When humans solve math problems, we often skip steps we consider 'obvious.' But what’s obvious to an expert isn’t always clear to an AI—or a student. These missing steps, dubbed Thought Leaps, create reasoning gaps that hinder learning and generalization. The researchers found that models trained on datasets with Thought Leaps performed up to 27.83% worse than those trained on complete reasoning chains.
The Solution: CoT-Bridge
The team developed CoT-Bridge, a specialized model trained on ScaleQM+, a dataset built by strategically removing steps from structured math problems and then restoring them. CoT-Bridge does two things:
- Detects Thought Leaps—identifying where steps are missing.
- Generates Bridging Steps—filling in the gaps with coherent explanations.
Key Results
- +5.87% improvement on NuminaMath, a challenging math benchmark.
- +3.1% boost in reinforcement learning performance when used as a starting point.
- Better generalization to out-of-domain logical reasoning tasks, suggesting broader applicability.
Why It Matters
Thought Leaps aren’t just a nuisance—they fundamentally limit how well AI models learn. By fixing them, CoT-Bridge acts as a plug-and-play upgrade for existing training pipelines, enhancing everything from fine-tuning to reinforcement learning. As AI tackles more complex reasoning tasks, ensuring complete, step-by-step explanations could be the key to unlocking deeper understanding.
What’s Next
The team plans to explore scaling CoT-Bridge to larger models and expanding beyond math into fields like law and medicine. For now, their work highlights an often-overlooked flaw in AI reasoning—and offers a clever way to fix it.