RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization

📰 ArXiv cs.AI

Learn to balance logical grounding and fluency in large language models using RLearner-LLM with Hybrid Direct Preference Optimization

advanced Published 7 May 2026

Action Steps

Implement Direct Preference Optimization (DPO) to optimize language model performance
Identify and address the verbosity bias in standard preference signals
Apply Hybrid-DPO to balance logical grounding and fluency in language models
Evaluate the logical alignment of language models using NLI entailment metrics
Fine-tune language models using RLearner-LLM with Hybrid-DPO to improve performance

Who Needs to Know This

NLP researchers and engineers can benefit from this technique to improve the logical correctness of their language models, while also maintaining fluency

Key Insight

💡 Hybrid Direct Preference Optimization can help balance logical grounding and fluency in large language models

Key Takeaways

Learn to balance logical grounding and fluency in large language models using RLearner-LLM with Hybrid Direct Preference Optimization

Full Article

Title: RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization

Abstract:
arXiv:2605.04539v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO), the efficient alternative to PPO-based RLHF, falls short on knowledge-intensive generation: standard preference signals from human annotators or LLM judges exhibit a systematic verbosity bias that rewards fluency over logical correctness. This blindspot leaves a logical alignment gap -- SFT models reach NLI entailment of only 0.05-0.22 despite producing fluent text. We propose RLearner-LLM with Hybrid-DPO: an

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