Warm Up Before You Train: Unlocking General Reasoning in Resource-Constrained Settings
Safal Shrestha, Minwu Kim, Aadim Nepal, Anubhav Shrestha, Keith Ross
2025-05-21
Summary
This paper talks about a smart way to train AI models to think better even when there's not much data or computing power available.
What's the problem?
Training AI to handle complex reasoning tasks usually requires tons of data and resources, which many organizations can't afford, making it hard to create useful models.
What's the solution?
Researchers used a two-step approach: first 'warming up' the AI with logic puzzles to build basic reasoning skills, then fine-tuning it with a special training method on small amounts of task-specific data.
Why it matters?
This method helps create smarter AI systems that learn faster and work better in real-world situations where data and computing resources are limited, making advanced AI more accessible to everyone.
Abstract
A two-stage training strategy involving a warm-up phase with distillation of logic puzzles followed by RLVR training on limited domain-specific data improves reasoning capabilities and sample efficiency in LLMs.