Impact of Multitask Fine-Tuning on Qwen2.5 Alignment Capabilities via AlpacaEval and Chatbot Arena Preference Scores

Abstract

Abstract: Reward models (RMs) play a crucial role in Reinforcement Learning from Human Feedback by serving as proxies for human preferences in aligning large language models. However, they suffer from various biases which could lead to reward hacking. In this paper, we identify a model preference bias in RMs, where they systematically assign disproportionately high scores to responses from certain policy models, leading to unfair judgments. To mitigate this bias, we propose a calibration method named CHatbot Arena calibrated Reward Modeling (CHARM) that leverages Elo scores from the Chatbot Arena to con

Research Question

What is the effect of multitask fine-tuning on the alignment capabilities of Qwen2.5, as evaluated by preference scores on AlpacaEval and Chatbot Arena?

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