In this work, we demonstrate that small language models (SLMs), specifically a 100M parameter GPT-2 model, can achieve competitive performance in multitask prompt generation tasks while requiring only a fraction of the computational resources needed by large language models (LLMs). Through a novel combination of upside-down reinforcement learning and synthetic data distillation from a powerful LLM, Llama-3, we train an SLM that achieves relevance scores within 5% of state-of-the-art models, including Llama-3, Qwen2, and Mistral, despite being up to 80 times smaller, making it highly suitable for resource-constrained and real-time applications. This study highlights the potential of SLMs as efficient multitask learners in multimodal settings, providing a promising alternative to LLMs for scalable, low-latency deployments.
View on arXiv@article{lin2025_2502.09854,
title={ Efficient Multitask Learning in Small Language Models Through Upside-Down Reinforcement Learning },
author={ Yu-Chen Lin and Sanat Sharma and Hari Manikandan and Jayant Kumar and Tracy Holloway King and Jing Zheng },
journal={arXiv preprint arXiv:2502.09854},
year={ 2025 }
}