Awesome LLM Reasoning is a curated collection of papers and resources focused on reasoning capabilities in large language models and multimodal language models.
The collection addresses the challenge of understanding and unlocking reasoning abilities in LLMs and MLLMs by organizing research across multiple dimensions. It structures papers into surveys, analysis work, and technique categories that distinguish between reasoning in standard language models as an emergent ability, multimodal reasoning approaches, and methods for scaling reasoning capabilities to smaller models. This organizational approach helps developers and researchers navigate the rapidly expanding literature on how language models can be prompted and trained to perform complex reasoning tasks.
Developers should use this collection if they are building systems that require language models to solve multi-step problems, engage in symbolic reasoning, or combine visual and textual information for reasoning tasks. It suits research teams exploring prompt engineering techniques like chain-of-thought prompting, as well as practitioners implementing reasoning-focused applications. The repository complements a related benchmarking tool designed specifically for testing symbolic reasoning abilities in language models, making it useful for both understanding the state of the field and evaluating model performance.
The project maintains an active curation of recent research, with survey papers from the current year included alongside foundational work. Contributions are welcomed through a standard process, indicating ongoing community engagement with the resource.
