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This AI Paper Introduces TinyLoRA, A 13-Parameter Fine-Tuning Method That Reaches 91.8 Percent GSM8K on Qwen2.5-7B

MarkTechPost / 3/25/2026

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Key Points

  • Researchers from Meta FAIR, Cornell, and Carnegie Mellon introduce TinyLoRA, a fine-tuning parameterization designed to let LLMs learn reasoning with extremely few trainable parameters.
  • The method can be scaled down to as few as 13 parameters under shared/low-rank settings, pushing the limits of parameter-efficient training.
  • In experiments fine-tuning Qwen2.5-7B, TinyLoRA reportedly reaches 91.8% on the GSM8K benchmark, indicating strong performance despite the tiny training footprint.
  • The work suggests that careful parameter sharing and LoRA-style adaptation can maintain reasoning quality while greatly reducing training cost and complexity.

Researchers from FAIR at Meta, Cornell University, and Carnegie Mellon University have demonstrated that large language models (LLMs) can learn to reason using a remarkably small number of trained parameters. The research team introduces TinyLoRA, a parameterization that can scale down to a single trainable parameter under extreme sharing settings. Using this method on a […]

The post This AI Paper Introduces TinyLoRA, A 13-Parameter Fine-Tuning Method That Reaches 91.8 Percent GSM8K on Qwen2.5-7B appeared first on MarkTechPost.

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This AI Paper Introduces TinyLoRA, A 13-Parameter Fine-Tuning Method That Reaches 91.8 Percent GSM8K on Qwen2.5-7B | AI Navigate