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Life sciences · Preprint
arXiv · September 4, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is a preprint describing a machine learning framework combining QLoRA, symbolic routing, and reinforcement learning from verifier rewards to improve reasoning transparency and answer correctness in educational question answering. The work demonstrates gains in reasoning explainability (P3: 50.68% to 72.20%) and system-level answer reliability on a single held-out test set, but has not undergone peer review and lacks external validation or comparison to established baselines.
Preprint. Educational logic and physics problems; no human subjects enrolled.. Intervention: Verifier-guided explainable reasoning framework combining gold-anchored QLoRA, task-aware mixture-of-experts routing, and group-relative RLVR with self-consistency and symbolic verification.. n = 438.
RLVR increases P3 (reasoning depth and explainability) from 50.68% to 72.20% on 438 held-out examples Hybrid P1 (answer correctness) remains approximately stable at 55.94% after RLVR Self-consistency improves model-only P1 from 48.86% to 50.23%
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This is an unrefereed preprint describing a machine learning method development study on a held-out test set of 438 examples, with no peer review, no clinical validation, and no comparison to established baselines or human performance.
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Large language models (LLMs) show strong reasoning ability, but their explanations can remain inconsistent, weakly grounded, or difficult to verify. We propose a verifier-guided explainable reasoning framework for transparent educational question answering that combines gold-anchored QLoRA, task-aware symbolic routing, and group-relative RLVR. Qwen2.5-3B-Instruct is first adapted with field-weighted QLoRA supervision anchored to authoritative answers. A lightweight router then assigns logic problems to a FOL/Z3 verifier and physics problems to a formula- and unit aware symbolic solver. Verifier feedback is further used to support candidate evaluation, self-revision, and reward construction during RLVR. Candidate responses are evaluated along three complementary dimensions: P1 for answer correctness, P2 for evidence or unit consistency, and P3 for reasoning depth and explainability. At inference, gold-free self-consistency aggregates multiple candidate responses before an optional question-only physics verifier performs conservative system-level correction. On 438 held-out examples, RLVR increases P3 from 50.68% to 72.20%, while hybrid P1 remains approximately stable at 55.94%. Self-consistency improves model only P1 from 48.86% to 50.23%, with symbolic verification providing the remaining hybrid gain. These results indicate that RLVR primarily strengthens explicit reasoning structure, while symbolic verification complements the neural policy by improving answer reliability at the system level.
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