QuantumQA: Enhancing Scientific Reasoning via Physics-Consistent Dataset and Verification-Aware Reinforcement Learning

TL;DR

QuantumQA introduces a physics-consistent dataset and a verification-aware reinforcement learning approach to improve the scientific reasoning capabilities of large language models in quantum mechanics.

Contribution

The paper presents a novel dataset and a verification-aware reinforcement learning method that enhances LLM reliability in scientific domains with strict physical constraints.

Findings

QuantumQA's dataset ensures scientific rigor through hybrid verification.

The verification-aware reward model improves model performance over baselines.

An 8B model achieves competitive results with proprietary models.

Abstract

Large language models (LLMs) show strong capabilities in general reasoning but typically lack reliability in scientific domains like quantum mechanics, which demand strict adherence to physical constraints. This limitation arises from the scarcity of verifiable training resources and the inadequacy of coarse feedback signals in standard alignment paradigms. To address the data challenge, we introduce QuantumQA, a large-scale dataset constructed via a task-adaptive strategy and a hybrid verification protocol that combines deterministic solvers with semantic auditing to guarantee scientific rigor. Building on this foundation, we propose the verification-aware reward model (VRM) tailored for Reinforcement Learning with Verifiable Rewards (RLVR), which employs an adaptive reward fusion (ARF) mechanism to dynamically integrate deterministic signals from a scientific execution suite (SES) with multidimensional semantic evaluations for precise supervision. Experimental results demonstrate that our method consistently outperforms baselines and general-purpose preference models. Notably, our optimized 8B model achieves performance competitive with proprietary models, validating that incorporating verifiable, rule-based feedback into the reinforcement learning loop offers a parameter-efficient alternative to pure scaling.