arXiv:2509.21013v4 Announce Type: replace Abstract: Given the prohibitive cost of pre-training large language models, it is essential to leverage smaller proxy models to optimize datasets before scaling up. However, this approach becomes challenging for reasoning capabilities, which exhibit emergent behavior that only appear reliably at larger model sizes, often exceeding 7B parameters. To address this, we introduce rBridge, showing that small proxies ($\leq$1B) can effectively predict large-model reasoning by aligning more closely with (1) the pre-training objective and (2) the target task. r
The increasing computational cost of developing large language models necessitates new methods for efficient optimization and pre-training dataset curation.
This research offers a method to significantly reduce the cost and time associated with training large language models by using smaller, more accessible proxy models for early-stage evaluation, thus democratizing LLM development.
The ability to predict large LLM reasoning performance with small models changes the LLM development paradigm, potentially making advanced research and model fine-tuning more accessible to broader groups.
- · AI researchers
- · Smaller AI companies
- · Open-source AI community
- · Cloud computing providers
- · Companies reliant on proprietary large-scale LLM datasets
Reduced computational expense in LLM development and fine-tuning.
Faster iteration cycles for LLMs, leading to more rapid advancements and diversified applications.
Lower barriers to entry in advanced AI development, potentially fostering more competition and innovation in the AI sector globally.
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Read at arXiv cs.LG