AIBullisharXiv – CS AI · 5h ago7/10
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RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization
Researchers introduce RLearner-LLM, a hybrid optimization method that combines NLI (Natural Language Inference) signals with LLM verification to address a critical flaw in Direct Preference Optimization: the tendency to reward verbose but logically incorrect outputs. The approach achieves up to 6x improvement in logical consistency across academic domains while maintaining inference speed, demonstrating that logic-aware metrics outperform traditional LLM-based evaluation for knowledge-intensive tasks.
🧠 GPT-4