Ladders of Thought: A Self-Evolving Curriculum of Progressively Simplified Reasoning Traces

2026-09-21

AI Summary

本文提出Ladders-of-Thought框架，通过逐步简化问题和自适应课程来提高小规模语言模型的推理能力，优于知识蒸馏方法。

Abstract

Large language models (LLMs) excel at reasoning when scaled to hundreds of billions of parameters, but small- and mid-scale models remain brittle reasoners even with knowledge distillation (KD). We present Ladders-of-Thought (LoT), a framework that improves reasoning by combining progressive question rewrites with a self-evolving curriculum. LoT automatically generates semantically faithful but easier variants of reasoning problems, organizes them into difficulty buckets using step-based measures, and employs a self-evolving bandit scheduler to allocate training adaptively. Evaluated on two reasoning domains, math and multi-hop reasoning, across 1-8B models from different families, LoT consistently improves over KD. It delivers large gains on arithmetic tasks (e.g., +32 percentage points on AddSub, +25pp on SVAMP), +2-8pp improvements on in-domain test splits, and strong though dataset-dependent benefits on multi-hop reasoning (e.g., +16pp on QASC, +25pp on StrategyQA). LoT also converges faster than staged curricula, highlighting the value of adaptive progression. These results show that progressive rewrites coupled with adaptive curricula provide a simple yet effective recipe for strengthening reasoning in smaller LLMs.

Problem

• language models
• reasoning
• knowledge distillation

Innovation

• Ladders-of-Thought
• progressive question rewrites
• self-evolving curriculum
• adaptive training
• reasoning improvement