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Chen Lai

2 accepted papers

2026

Exploring the Limits of Sub-Billion Language Model Reasoners with Open Training Recipes

ICLR 2026poster

The paradigm shift in large language models (LLMs) from instinctive responses to chain-of-thought (CoT) reasoning has fueled two prevailing assumptions: (1) reasoning capabilities only emerge in sufficiently large models, and (2) such capabilities require training on massive datasets. While the firs…

Cited by 0SourceScholar
2024

MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

ICML 2024poster

This paper addresses the growing need for efficient large language models (LLMs) on mobile devices, driven by increasing cloud costs and latency concerns. We focus on designing top-quality LLMs with fewer than a billion parameters, a practical choice for mobile deployment. Contrary to prevailing bel…