SalesforceAIResearch/Elastic-Reasoning
Make reasoning models scalable
This project helps AI developers and researchers make large language models (LLMs) more efficient and reliable when solving complex problems like math or coding. It takes an LLM and training data, then teaches the model to separate its "thinking" process from its "solution" output. The result is an LLM that can produce accurate answers using fewer computational resources, even when time or memory are limited.
No commits in the last 6 months.
Use this if you are a machine learning engineer or researcher working with large language models and need to improve their performance and reliability under tight computational budgets for reasoning tasks.
Not ideal if you are a non-developer seeking an out-of-the-box solution or if your primary goal is not optimizing LLM reasoning efficiency and scalability.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
May 31, 2025
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