# Over-Reasoning and Redundant Calculation of Large Language Models

arXiv: https://arxiv.org/abs/2401.11467 · HTML: https://mlsanity.com/paper/2401.11467/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Placed in Mathematical reasoning, one of 17 directions in LLM reasoning.
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 21 Jan 2024.

## Placed in

- LLM reasoning / [Mathematical reasoning](https://mlsanity.com/lr/mathematical-reasoning/)

## Builds on (most cited first, 7 of 7)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [The Flan Collection: Designing Data and Methods for Effective Instruction Tuning](https://mlsanity.com/paper/2301.13688/) (2023)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)

## Built on by (most cited first, 27 of 27)

- [Self-Training Elicits Concise Reasoning in Large Language Models](https://mlsanity.com/paper/2502.20122/) (2025)
- [How Well do LLMs Compress Their Own Chain-of-Thought? A Token Complexity Approach](https://mlsanity.com/paper/2503.01141/) (2025)
- [Evaluating Step-by-step Reasoning Traces: A Survey](https://mlsanity.com/paper/2502.12289/) (2025)
- [Efficient Inference for Large Reasoning Models: A Survey](https://mlsanity.com/paper/2503.23077/) (2025)
- [How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?](https://mlsanity.com/paper/2404.03302/) (2024)
- [Oolong: Evaluating Long Context Reasoning and Aggregation Capabilities](https://mlsanity.com/paper/2511.02817/) (2025)
- [Large Language Model as an Assignment Evaluator: Insights, Feedback, and Challenges in a 1000+ Student Course](https://mlsanity.com/paper/2407.05216/) (2024)
- [Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning](https://mlsanity.com/paper/2510.10103/) (2025)
- [Concise Reasoning, Big Gains: Pruning Long Reasoning Trace with Difficulty-Aware Prompting](https://mlsanity.com/paper/2505.19716/) (2025)
- [Lost at the Beginning of Reasoning](https://mlsanity.com/paper/2506.22058/) (2025)
- [Verbosity neq Veracity: Demystify Verbosity Compensation Behavior of Large Language Models](https://mlsanity.com/paper/2411.07858/) (2024)
- [FormalML: A Benchmark for Evaluating Formal Subgoal Completion in Machine Learning Theory](https://mlsanity.com/paper/2510.02335/) (2025)
- [Do Chatbot LLMs Talk Too Much? The YapBench Benchmark](https://mlsanity.com/paper/2601.00624/) (2026)
- [When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models](https://mlsanity.com/paper/2510.00626/) (2025)
- [CoTJudger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRMs](https://mlsanity.com/paper/2603.07078/) (2026)
- [Attention Overflow: Language Model Input Blur during Long-Context Missing Items Recommendation](https://mlsanity.com/paper/2407.13481/) (2024)
- [From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement](https://mlsanity.com/paper/2509.22144/) (2025)
- [Better Starts, Better Ends: Bootstrapped Iterative Self-Reasoning Distillation for Compressed Reasoning](https://mlsanity.com/paper/2607.15736/) (2026)
- [ARISE: An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning Models](https://mlsanity.com/paper/2510.06014/) (2025)
- [Focused Chain-of-Thought: Efficient LLM Reasoning via Structured Input Information](https://mlsanity.com/paper/2511.22176/) (2025)
- [Explainable reinforcement learning from human feedback to improve alignment](https://mlsanity.com/paper/2512.13837/) (2025)
- [Judge Model for Large-scale Multimodality Benchmarks](https://mlsanity.com/paper/2601.06106/) (2026)
- [Think Less, Know More: State-Aware Reasoning Compression with Knowledge Guidance for Efficient Reasoning](https://mlsanity.com/paper/2604.09150/) (2026)
- [CRISP: Compressing Redundancy in Chain-of-Thought via Intrinsic Saliency Pruning](https://mlsanity.com/paper/2604.17297/) (2026)
- [Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detection](https://mlsanity.com/paper/2410.03904/) (2024)
- [Adaptive Reasoning Executor: A Collaborative Agent System for Efficient Reasoning](https://mlsanity.com/paper/2510.13214/) (2025)
- [The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models](https://mlsanity.com/paper/2606.29799/) (2026)

## Abstract (arXiv)

Large language models (LLMs) can solve problems step-by-step. While this chain-of-thought (CoT) reasoning boosts LLMs' performance, it is unclear if LLMs textitknow when to use CoT and whether those CoT are always necessary to answer the question. This paper shows that LLMs tend to generate redundant calculations and reasoning on a manually constructed math QA dataset, GSM8K-Zero. GSM8K-Zero is constructed such that the questions can be answered without any calculations, but LLMs, including Llama-2 models and Claude-2, tend to generate lengthy and unnecessary calculations to answer the questions. We also conduct experiments to explain why LLMs generate redundant calculations and reasonings. GSM8K-Zero is publicly available at https://github.com/d223302/Over-Reasoning-of-LLMs and https://huggingface.co/datasets/dcml0714/GSM8K-Zero.