Reward Modeling for Reinforcement Learning-Based LLM Reasoning: Design, Challenges, and Evaluation
It addresses the fundamental issue of reward design for improving LLM reasoning, which is crucial for building robust and trustworthy AI systems, but is largely incremental as it integrates existing research threads into a systematic framework.
This work tackles the problem of inconsistent and unreliable reasoning in Large Language Models (LLMs) by emphasizing the critical role of reward modeling in reinforcement learning-based fine-tuning, introducing a unifying framework called Reasoning-Aligned Reinforcement Learning (RARL) to systematize reward paradigms and analyze challenges like reward hacking and evaluation vulnerabilities.
Large Language Models (LLMs) demonstrate transformative potential, yet their reasoning remains inconsistent and unreliable. Reinforcement learning (RL)-based fine-tuning is a key mechanism for improvement, but its effectiveness is fundamentally governed by reward design. Despite its importance, the relationship between reward modeling and core LLM challenges--such as evaluation bias, hallucination, distribution shift, and efficient learning--remains poorly understood. This work argues that reward modeling is not merely an implementation detail but a central architect of reasoning alignment, shaping what models learn, how they generalize, and whether their outputs can be trusted. We introduce Reasoning-Aligned Reinforcement Learning (RARL), a unifying framework that systematizes diverse reward paradigms for multi-step reasoning. Within this framework, we present a taxonomy of reward mechanisms, analyze reward hacking as a pervasive failure mode, and examine how reward signals unify challenges ranging from inference-time scaling to hallucination mitigation. We further critically evaluate existing benchmarks, highlighting vulnerabilities such as data contamination and reward misalignment, and outline directions for more robust evaluation. By integrating fragmented research threads and clarifying the interplay between reward design and fundamental reasoning capabilities, this work provides a foundational roadmap for building reasoning models that are robust, verifiable, and trustworthy.