arXiv:2512.07407v3 Announce Type: replace Abstract: Language models frequently produce plausible yet incorrect reasoning traces that are difficult to verify. We investigate fine-tuning models to use Prolog as an external symbolic reasoning tool, training Qwen2.5-3B-Instruct with Group Relative Policy Optimization (GRPO) on a cleaned version of GSM8K (which we release as gsm8k-prolog-prover). We systematically vary prompt structure, reward composition (execution, syntax, semantics, structure), and inference protocol (single-try, multiple-try, and two agentic modes). Our reinforcement learning a
The proliferation of language models and growing challenges with their hallucination and reasoning capabilities are driving research into robust, verifiable AI tool integration.
Improving the accuracy and verifiability of AI reasoning is critical for deploying AI in sensitive applications and for enhancing the robustness of autonomous AI agents.
This development suggests a potential path toward more reliable and less error-prone AI systems by combining neural networks with symbolic reasoning.
- · AI developers
- · AI safety researchers
- · Companies building AI agents
- · AI models prone to hallucination
- · Solutions relying solely on black-box neural networks
Language models become significantly more reliable in tasks requiring logical reasoning.
This improved reliability leads to faster adoption of AI agents in mission-critical applications.
The integration of symbolic tools could mitigate some existential risks associated with unaligned or unreliable advanced AI systems.
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