Finding the Minimal Parameter Budget for Implicit Reasoning: A Data Complexity Driven Scaling Law for Language Models
arXiv:2504.03635v4 Announce Type: replace-cross Abstract: Reasoning is a core capability of language models (LMs), yet it remains unclear how much model capacity is necessary to support reasoning during pretraining. In this work, we study the minimal parameter budget required for implicit reasoning, defined as the ability to infer new facts from learned knowledge without explicit chain-of-thought supervision. To isolate this phenomenon, we pretrain LMs from scratch in a controlled synthetic environment that mimics the structure and distribution of real-world knowledge graphs, and evaluate thei
The rapid advancement and deployment of large language models necessitates a deeper understanding of their fundamental capabilities and resource requirements.
Understanding the minimal parameter budget for implicit reasoning directly impacts the efficiency and accessibility of advanced AI, influencing research directions and practical applications.
This research provides a more precise framework for optimizing LM design for reasoning tasks, potentially leading to more efficient and powerful models without exponential scaling of parameters.
- · AI researchers
- · Model developers
- · Efficient AI deployment
- · Inefficient large-scale model trainers
More resource-efficient language models become capable of sophisticated reasoning without explicit Chain-of-Thought prompting.
Reduced compute costs for deploying powerful AI models could democratize access to advanced AI capabilities.
This could accelerate the development of more autonomous and capable AI agents, as reasoning becomes intrinsically more accessible.
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