GAIR-NLP/LIMO

• hightopics#1

  Add relevant topics to the repository

  Why:

  COPY-PASTE FIX

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  large-language-models, llm-reasoning, few-shot-learning, minimal-data, nlp, ai-research, dataset, model, colm-2025

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• highlicense#2

  Add an MIT LICENSE file and declare the license in README

  Why:

  CURRENT

  (no LICENSE file detected)

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  Create a file named 'LICENSE' in the repository root with the following content:
  
  MIT License
  
  Copyright (c) [YEAR] [COPYRIGHT HOLDER]
  
  Permission is hereby granted, free of charge, to any person obtaining a copy
  of this software and associated documentation files (the "Software"), to deal
  in the Software without restriction, including without limitation the rights
  to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  copies of the Software, and to permit persons to whom the Software is
  furnished to do so, subject to the following conditions:
  
  The above copyright notice and this permission notice shall be included in all
  copies or substantial portions of the Software.
  
  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  SOFTWARE.
  
  Also, add the following line to your README, for example, in the 'About' section or near the top: "This project is licensed under the MIT License - see the LICENSE file for details."

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• mediumreadme#3

  Add an introductory paragraph to the README emphasizing minimal data reasoning

  Why:

  COPY-PASTE FIX

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  Insert the following paragraph immediately after the initial links section in the README:
  
  "LIMO (Less Is More for Reasoning) is a research project presented at COLM 2025, focused on achieving robust reasoning performance in Large Language Models using significantly fewer training examples. We provide a complete, open-source pipeline for collecting high-quality human preference data, enabling models to achieve competitive results with minimal data, as demonstrated by LIMO's performance with only 817 training samples."

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  How can I train large language models for complex reasoning with minimal data?

  you: not recommended
  AI recommended (in order):

  1. OpenAI GPT-4
  2. OpenAI GPT-3.5 Turbo
  3. Anthropic Claude 2
  4. Anthropic Claude Instant
  5. Hugging Face Transformers Library
  6. Google PaLM 2
  7. Google Gemini
  8. LangChain
  9. Weights & Biases Prompts
  10. Helicone

  AI recommended 10 alternatives but never named GAIR-NLP/LIMO. This is the gap to close.

  Show full AI answer
• CATEGORY QUERY

  What LLM approaches provide robust reasoning performance using significantly fewer training examples?

  you: not recommended
  AI recommended (in order):

  1. GPT-3.5/GPT-4 (OpenAI API)
  2. FLAN-T5 / FLAN-UL2 (Google)
  3. Llama 2 (Meta) (facebookresearch/llama)
  4. SetFit (Hugging Face) (huggingface/setfit)
  5. Sentence Transformers (UKPLab/sentence-transformers)

  AI recommended 5 alternatives but never named GAIR-NLP/LIMO. This is the gap to close.

  Show full AI answer

• Metadata completeness

  fail

  Suggestion:

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of GAIR-NLP/LIMO?

  pass
  ✓AI named GAIR-NLP/LIMO explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• If a team adopts GAIR-NLP/LIMO in production, what risks or prerequisites should they evaluate first?

  pass
  ✓AI named GAIR-NLP/LIMO explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

• In one sentence, what problem does the repo GAIR-NLP/LIMO solve, and who is the primary audience?

  pass
  ✓AI named GAIR-NLP/LIMO explicitly

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?