Impact of Inference-Optimized Fine-Tuning on Llama-3.1-8B Robustness in Ruler Positional Encoding Tasks

Abstract

Abstract: In this work, we present Qwen3, the latest version of the Qwen model family. Qwen3 comprises a series of large language models (LLMs) designed to advance performance, efficiency, and multilingual capabilities. The Qwen3 series includes models of both dense and Mixture-of-Expert (MoE) architectures, with parameter scales ranging from 0.6 to 235 billion. A key innovation in Qwen3 is the integration of thinking mode (for complex, multi-step reasoning) and non-thinking mode (for rapid, context-driven responses) into a unified framework. This eliminates the need to switch between different models–

Research Question

To what extent do inference-optimized fine-tuning methods impact the robustness of Llama-3.1-8B on positional encoding tasks within the Ruler benchmark suite?

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