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Link to arxiv: https://arxiv.org/pdf/2602.04118

Large language models have recently shown impressive reasoning abilities, often learned through reinforcement learning and low-rank adaptation techniques like LoRA. But these approaches still assume that effective reasoning requires relatively large adaptation layers. This new paper challenges that assumption by asking a provocative question: how small can a reasoning update really be?

In this episode, we explore Learning to Reason in 13 Parameters, which introduces TinyLoRA, a method that compresses low-rank adapters down to the extreme — in some cases to just a single parameter. Instead of relying on large adaptation matrices, TinyLoRA demonstrates that reasoning behavior can be steered using ultra-minimal parameter updates, dramatically reducing the computational and memory footprint required to teach models new reasoning skills.

We break down:

• Why conventional LoRA and low-rank adapters hit a floor at model dimensionality,
• How TinyLoRA scales reasoning adapters down to near-zero parameter counts,
• What this reveals about where reasoning ability actually lives inside neural networks,
• And why tiny adaptation layers could reshape efficient fine-tuning, on-device intelligence, and rapid deployment.

The results suggest that reasoning competence may not require massive structural changes — only precisely targeted parameter nudges. This challenges assumptions about scaling, efficiency, and the true complexity of learned reasoning.