Efficient Multitask Learning in Small Language Models Through Upside-Down Reinforcement Learning

• URL: http://arxiv.org/abs/2502.09854v1
• Date: Fri, 14 Feb 2025 01:39:45 GMT
• Title: Efficient Multitask Learning in Small Language Models Through Upside-Down Reinforcement Learning
• Authors: Yu-Chen Lin, Sanat Sharma, Hari Manikandan, Jayant Kumar, Tracy Holloway King, Jing Zheng,
• Abstract summary: Small language models (SLMs) can achieve competitive performance in multitask prompt generation tasks. 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.
• Score: 8.995427413172148
• License:
• Abstract: 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.

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