TrustRL-Eval: An Interpretable Reinforcement Learning Framework for Predicting and Optimizing Large Language Model Response Quality in Multi-Step Reasoning Tasks
Keywords:
reinforcement learning, interpretability, large language models, multi-step reasoning, reward modeling, SHAP, trustworthiness, socio-technical systemsAbstract
Large language models have demonstrated remarkable capabilities in multi-step reasoning tasks, yet their reliability and response quality remain difficult to predict and optimize at scale. This paper introduces TrustRL-Eval, an interpretable reinforcement learning framework that integrates predictive reward modeling with attention-based interpretability mechanisms to forecast and enhance the quality of chain-of-thought outputs. The framework treats each reasoning step as a state in a Markov decision process, where a learned reward function approximates the likelihood of producing a correct final answer. By combining policy optimization with a SHAP-inspired attribution module, TrustRL-Eval provides per-step importance scores that enable operators to trace the causes of reasoning failures. We discuss the system-level architecture of the framework, emphasizing the trade-offs between predictive accuracy, computational overhead, and interpretability. Governance implications are examined through the lens of fairness and robustness, particularly when the framework is deployed across heterogeneous reasoning tasks and user populations. A case illustration involving multi-hop question answering demonstrates how TrustRL-Eval can reduce erroneous reasoning chains by approximately 23% compared to baseline reinforcement learning methods. The paper further explores deployment strategies for low-resource environments, sustainability considerations regarding energy consumption during iterative training, and policy recommendations for ensuring that interpretability does not come at the cost of model performance. TrustRL-Eval positions itself as a promising step toward trustworthy LLM-based reasoning systems, offering a blueprint for future research in interpretable reinforcement learning for large-scale socio-technical infrastructures.
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