Concise Reasoning, Big Gains: Pruning Long Reasoning Trace with Difficulty-Aware Prompting

TL;DR

This paper introduces a difficulty-aware prompting method that shortens reasoning traces in chain-of-thought prompting, reducing costs while maintaining or improving performance across multiple benchmarks.

Contribution

It proposes a dynamic, difficulty-based rewriting of reasoning traces, creating a concise dataset LiteCoT and training efficient reasoning models that outperform larger models trained on longer traces.

Findings

Shorter reasoning traces achieve comparable or better accuracy.

Models trained on LiteCoT outperform those trained on longer traces.

Significant reduction in inference tokens and training costs.

Abstract

Existing chain-of-thought (CoT) distillation methods can effectively transfer reasoning abilities to base models but suffer from two major limitations: excessive verbosity of reasoning traces and inadequate adaptability to problem difficulty. Long reasoning traces significantly increase inference costs, and uniform-length solutions prevent base models from learning adaptive reasoning strategies. To address these issues, we propose a difficulty-aware prompting (DAP) method to dynamically shorten reasoning traces without performance loss. In our approach, a large teacher model first judges each problem's difficulty and then rewrites its reasoning traces to an appropriate shorter length, yielding concise yet complete reasoning traces. Leveraging the DAP pipeline, we curate a distilled dataset called LiteCoT consisting of 100K concise reasoning examples, with solutions averaging only 720 tokens (an order of magnitude shorter than typical CoTs). Using LiteCoT, we distilled a new family of reasoning models called Liter (1.5B, 7B, and 32B) based on the Qwen2.5 architecture. Experiments show that a student model fine-tuned on just 100K of these difficulty-pruned CoT samples outperforms a model distilled on 800K original Long CoT samples, while significantly reducing training and inference costs. Our method also generalizes well: across 11 diverse benchmarks, the shorter difficulty-aware CoTs achieve equal or better accuracy than Long chains, using far fewer tokens. For example, on the challenging AIME24 exam, our approach reaches $74.2\%$ Pass@1 using only about 5K inference tokens, surpassing other methods that consume many more tokens. Our code and data are available at https://github.com/Evanwu1125/LiteCoT.